Thierry Bonaudo
email: thierry.bonaudo@agroparistech.fr.

Frederic Wallet

Wallet Frédéric b Redlingshöfer Barbara

Niang Amadou

Elisabeth Rémy

Caroline Petit

Bonaudo Thierry

Rémy Elisabeth

Analysing and supporting territorial dynamics of agri-food systems: insights into the benefits and challenges of circular frameworks

Keywords: Agri-food systems, Circular economy, multi-criteria, sustainability, Materials flows analysis, Territorial development

published or not. The documents may come

Introduction

The ecological crisis is forcing societies to rethink how they function and how they manage resources. The question of the best allocation and use of resources, which is at the heart of regional economics, was intensified by the emergence in the 1960s of models that were critical of the impacts of industrial development on the environment and on social well-being [START_REF] Boulding | The Economics of the Coming Spaceship Earth[END_REF][START_REF] Georgescu-Roegen | The Entropy Law and the Economic Process[END_REF]. They led to the emergence of different coexisting conceptual frameworks and analytical approaches that seek to take greater account of the limits of the biosphere. In the late 1980s, the Brundtland Report highlighted the concept of sustainable development. This opened the way to new public policies and numerous field experiments, which quite significantly preceded the incorporation of sustainability issues into regional economics models (Archibugi and Nijkamp, 1990).

Today, new frameworks inspired by ecology are emerging and becoming institutionalised. In addition to the efficient use of resources, these frameworks share a common foundation in their attention to the entire life cycle of products and to the locally closed loops of material and energy cycles in the quest to improve the environmental and socio-economic performances of anthropic systems. While industrial activities were originally the principal field of application of these frameworks, we are witnessing their gradual extension to other economic sectors and to all territories. In this article, we mainly employ the frameworks of circular economics (CE) to cover these new frameworks inspired by ecology (social or territorial metabolism, industrial and territorial ecology, etc.). The CE is defined as an economic system of trade and production which, at every stage in the life-cycle of products, seeks to make more efficient use of resources and to reduce environmental impacts while contributing to the well-being of individuals. To this end, CE promotes sustainable supply systems, responsible consumption, eco-design, longer product life, and waste management that focuses on recycling. CE approaches notably draw on the quantitative and qualitative analysis of flows of materials and energy, as well as the spatial and territorial embeddedness and the governance of those flows [START_REF] Barles | Écologies urbaine, industrielle et territoriale[END_REF][START_REF] Bonaudo | Analyser une transition agro-alimentaire par les flux d'azote : Aussois un cas d'étude du découplage progressif de la production et de la consommation[END_REF].

A fundamental aspect of territorial functioning is how actors exploit and transform territory in order to produce the means of subsistence within it, in particular food. Agri-food systems are therefore, once again, at the heart of many strategies for ecological transition and sustainable territorial development. Agri-food systems encompass all the activities and functions involved in the production, storage, processing and the consumption of food. The management of by-products, wastes and losses are included in the system. Agri-food systems are composed of sub-systems like the farming system, waste management system, input supply system, etc. and interact with other key systems like the energy system and trade system (FAO, 2018). They are complex systems, operating at multiple levels of scale. Often criticised for the pollution and damage to biodiversity that they cause, agri-food systems also bring numerous territorial innovations in the production of bioproducts, the valorisation of biowaste (energy, fertiliser) and the limitation of environmental impacts. Today, these ideas are reshaping practices and institutional frameworks, as well as the conceptual frameworks through which we think about territorial development. In particular, they have led to a rethink about the notion of resources, wastes and their valorisation, with an emphasis on their limitations and the gridlock of linear systems. Finally, they emphasise the possibilities inherent in innovative partnerships designed to reinforce the reterritorialisation of activities and circular approaches. Circularity is becoming a goal in its own right, challenging the location of and interactions between activities and hence regional economics models.

New public policies and field experiments are thus emerging through the debate over the transition and relocation and resilience of systems [START_REF] Bhamra | Resilience: the concept, a literature review and future directions[END_REF]. There is, for example, the model of cities and territories in transition, based on community discussions that aim to promote local energy reductions and thereby diminish dependency on fossil energy sources [START_REF] Hopkins | Manuel de transition : de la dépendance au pétrole à la résilience locale[END_REF]. The debate is underpinned by a growing number of interdisciplinary academic studies, including joint research involving territorial players, in order to understand the processes involved in the creation of knowledge that encourages diversity in territorial transitions. However, the question of the spatial and territorial dimension of CE strategies has so far received little scholarly attention, despite the desire to see them implemented in regulatory provisions and public intervention strategies [START_REF] Niang | L'économie circulaire : quels enjeux de développement pour les territoires ?[END_REF]. We introduce our argument by showing how regional economics is converging through the CE approach and we will then present the objective of this article.

Convergence between circular economics and regional economics

Circular economics and regional economics have developed separately without the question of the connections between them being explicitly raised. However, the territorial grounding of activities is an important aspect of CE. Conversely, the question of methods of resource management is central in regional science models. Therefore, we are now seeing a gradual and reciprocal integration of approaches. The initial regional science models [START_REF] North | Location Theory and Regional Economic Growth[END_REF], founded on the quest for competitive advantages through reduced transport costs and the efficient allocation of production resources, were characterised by their lack of interest in environmental matters. In the 1970s, attempts to rediscover the benefits of specialist clusters in the relations between firms by reactivating an interest in the industrial district model [START_REF] Marshall | Industry and Trade[END_REF][START_REF] Becattini | The Marshallian Industrial District as a Socio-Economic Concept[END_REF], retained the linear principles governing production models. A complex and dynamic combination of relations of competition and cooperation, functional complementarities and flexibility within networks of businesses belonging to a single sector then became the dominant approach to an effective solution [START_REF] Piore | The Second Industrial Divide[END_REF]. This "territorial turn" (Pecqueur, 2006) emphasised the largely endogenous nature of regional development processes.

These theoretical models provide a conceptual arsenal that considers the diversity of configurations in the territorialised organisational forms of production systems. In particular, they place an emphasis on relations of complementarity within cluster value chains [START_REF] Porter | Clusters and the new economics of competition[END_REF], issues of quality and resource specification [START_REF] Colletis | Révélation de ressources spécifiques et coordination située[END_REF] and even the role of local environments (Aydalot, 1986) and relations of proximity [START_REF] Rallet | Proximity and localization[END_REF] in innovation processes. They would therefore provide a framework for understanding territorial approaches to CE. One way of considering the contribution of regional sciences to CE is to look at the dynamics of innovation and territorial development associated with the establishment of circular approaches [START_REF] Bocken | A literature and practice review to develop sustainable business model archetypes[END_REF]. Research on the governance of territorial innovation systems [START_REF] Cooke | The governance of innovation in Europe[END_REF] has shown that the drivers of territorial development combine dynamics both of production and governance. The interplay between the geographical proximity and the organisational proximity between businesses and other local actors, leads to specific governance processes that make possible the territorial innovations entailed in the sharing of know-how and knowledge and in collective learning [START_REF] Wallet | Regional Development in Rural Areas. Analytical tools and Public policies[END_REF].

A reflexive approach to the use of the CE framework to tackle the development of agri-food systems However, due to the complexity of agri-food systems, scientific research has often focused on the analysis of a sub-system (agricultural production or processing or waste management) by mobilising a specific method (material flow analysis, governance of the sector, ...). Despite these difficulties, it is essential to think of a programmatic approach to promote the emergence of holistic, multi-criteria and multidisciplinary analyses necessary for the sustainable transitions of agri-food systems and their territory. In order to contribute to this reflection, we have mobilised five case studies to develop a reflexive approach to the use of circularity framework to tackle agri-food systems at territorial scale. The different case studies employ different methods of analysis of flows and of the governance of those flows. The data acquisition and processing methods are detailed in the references given for each case study. Our approach is exploratory and we assume a certain heterogeneity of the case studies to cast a particular light on the production and use of bioresources, and on closed-loop cycles, and illustrate the diversity of fields in which circular economy can be applied. Our goal is therefore to show the strengths and practical and conceptual challenges of these frameworks from the perspective of sustainable territorial development. This will then lead into a discussion of three major dimensions to be considered in the perspective of circular agri-food systems: (i) advantages and limitations of territorial metabolism approach, (ii) unexpected effects of material circularization, (iii) the consideration of system complexity, diversity and resilience.

Presentation of case studies linked to agri-food systems

The circular economy (CE) is defined as an economic system of trade and production which, at every stage in the life-cycle of products, seeks to make more efficient use of resources and to reduce environmental impacts while contributing to the well-being of individuals. To this end, CE implies a net reduction of material flows through very low growth rate. It promotes sustainable supply systems, responsible consumption, eco-design, longer product life, and waste management that focuses on recycling. In this respect, the circular economy is not the equivalent of green growth [START_REF] Arnsperger | Vers une économie authentiquement circulaire[END_REF]. CE approaches notably draw on the quantitative and qualitative analysis of flows of materials and energy, as well as territorial embeddedness and the governance of those flows [START_REF] Bonaudo | Analyser une transition agro-alimentaire par les flux d'azote : Aussois un cas d'étude du découplage progressif de la production et de la consommation[END_REF]. Agri-food systems encompass all the activities and functions involved in the production, storage, processing and the consumption of food (Figure 1). The management of by-products, wastes and losses are included in the system. Agri-food systems are composed of sub-systems like the farming system, waste management system, input supply system, etc. and interact with other key systems like the energy system and trade system (FAO, 2018). They are complex systems, operating at multiple levels of scale.

The proposed case studies analyse how new ways of managing resources are established at territorial scale. They relate to changes in agri-food systems, in the broad sense, from agri-supply to waste management. Each case study is focused on a specific subsystem of the agri-food system (Figure 1). These cases deal with (1) the interactions between livestock farming and territory, (2) evaluation of food loss and food waste, (3) potential land pollution in urban green spaces due to recycling loops, (4) the governance of methanisation projects, and (5) finally the application of material flow representations for territorial development in the Saclay Plateau territory.

Interactions between livestock farming and territories

Livestock farming is a major factor in the equilibrium or disruption of biogeochemical cycles and biodiversity [START_REF] Bonaudo | Agroecological principles for the redesign of integrated crop-livestock systems[END_REF]. It is also an indispensable socio-economic player, and the diversity of the bioresources consumed and produced gives it a central role in the emergence of a circular economy [START_REF] Dourmad | Les productions animales dans la bioéconomie. In : Numéro spécial. De grands défis et des solutions pour l'élevage[END_REF]. The conceptual frameworks of circularity oblige us to rethink the linkage between livestock farming and land in terms of its consumption of resources, its dependency on inputs and its waste products. In this context, the analysis of the systems of material flows generated by livestock farming is a major issue for ecological transition. The studies focus on nitrogen flows in order to analyse together the biotechnical characteristics of livestock farming systems (species, density, consumption index…), their use of resources (land, feed, fertilisers…), their outputs (milk, meat, eggs), and also their waste products (manure).

Evaluation of food loss and waste

The evaluation of food loss and waste has become a matter of concern to public authorities. In France, public initiatives have proliferated through several legislative provisions and action programmes designed to prevent such waste. However, there exist few quantitative data, let alone spatial data, about food loss and waste. The information relies largely on one-off measurements using various methods, which fail to reflect the diversity of situations. According to Ademe (2016), the amount of food wasted annually per inhabitant is 150 kg (from production to consumption). This represents some 19% of total food production. This research assessed food flows and the associated loss and waste for the Île-de-France region.

Analysis of soil flows in urban green spaces

The lessons learned from the analysis of the development of green spaces on 49 sites in Paris since the 2000s show that the land is not cultivated in situ, but that gardening is developed on "buried off-soil" systems or off-soil trays created by introducing additional soil brought from the outskirts of Paris. The problem relates to the characterisation of these soil flows (past and present) into urban centres. For this, our research combines physio-chemical analyses of the quality of soil originating in former market garden areas and historical analyses of the systems of flows involved in the development of green spaces in Paris. This enables us to discuss the reality of these material loops.

Governance of methanisation projects

Methanisation is an interesting territorial strategy for the application of circular economy principles [START_REF] Bourdin | Understanding the problems of biogas production deployment in different regions: territorial governance matters too[END_REF]. The project developed by the Syndicat Mixte du Point Fort (SMPF), using a sorting centre and a methanisation unit, is a typical example of the territorial dynamics of organic waste valorisation. Located in Cavigny, the methanisation unit processes household wastes from 125 villages organised into five Public Intermunicipal Cooperation Establishments (EPCI) that are members of the SMPF. In this process, the fine part of the organic waste is sent to the methaniser to produce heat, to be reused in situ, or in electricity for resale. The intermediate part of the organic waste and the digestate are converted into compost for farming.

Material flow representations for a territorial development project

Since the 1990s, there has been a proliferation of public policy provisions for sustainable development and transition in the food, agricultural and energy sectors. These objectives are tackled at local scale by actors such as territorial authorities and civil society organisations that back endogenous development projects. Near Paris, the Saclay Plateau is an area characterised by the coexistence of big government ambitions around a science and technology cluster and local ambitions, embodied in local authorities and organisations. Since the early 2000s, a multi branch agri-urban civil society organisation has positioned itself as a federating and mediating structure between the various stakeholders of the territory. The goal of this organisation is to unite regional stakeholders around the challenge to enhance agricultural and natural areas. Among the flagship actions of the association is the organisation of territorial ecology workshops with local actors and researchers. The workshops made it possible to work on i) the characterisation of the territorial metabolism of the agri-food system and ii) the formulation of a possible contrasted future for the territory. Since then, these flow representations have been the basis of numerous discussions with local actors, questioning the scope of a metabolism analysis approach for territorial development.

Results

Based on these case studies, our results seek to illustrate the advantages and limitations of material flow analyses and the circularity framework for understanding and driving sustainable territorial dynamics.

3.1. Characterising the flows of materials in a territory for a multi-criteria analysis The major advantage of metabolism studies is to quantify the material flows involved in activities and to represent them in relation to each other within a single formal framework. This is a didactic approach that characterises the nature, the scale, the trend and the organisation of the flows of different activities within a territory. These studies also make it possible to link material flow systems to production models (practices and techniques). These quantitative analyses are multi-criteria and holistic. Two of our examples illustrate this essential work of characterising material flows and objectifying the links between activities.

Recent work has shown the contribution of agriculture within the local food system in the Saclay Plateau area, by evaluating the different sectors in relation to consumption needs [START_REF] Tedesco | Potential for recoupling production and consumption in peri-urban territories: The case-study of the Saclay plateau near Paris, France[END_REF]Petit et al., 2018). These studies were used to characterise the agri-food system and the hydrological flow of nutrients [START_REF] Verger | and water flows metabolism study in a peri-urban territory in France: The case-study of the Saclay plateau[END_REF]. The quantification of material flows has shown that food production and consumption are mostly disconnected. The average share of local food consumption in total food production is 12.9% and the average share of local production in total local consumption is 7.8% [START_REF] Tedesco | Potential for recoupling production and consumption in peri-urban territories: The case-study of the Saclay plateau near Paris, France[END_REF]. There are two different situations. For animal products, fruits and vegetables, local production and local consumption are strongly coupled (65% and 100% of local production is consumed locally), but local production is very low compared to consumption (respectively 4.4% and 4.8%). On the contrary, for cereal products, there is no coupling (0.2% of total production is consumed locally), while local production is very high and could potentially exceeds local needs. The agri-food metabolism patterns made visible the importance of the flows corresponding to imported fertilisers and to wastewater treatment and the marginal nature of flows corresponding to local short circuits. This objectification is essential for understanding the system and thinking about the potential of different actions to reconfigure the metabolism.

On the subject of food loss and waste, we quantified 792 kilotonnes of food waste present in household waste [START_REF] Redlingshöfer | Food waste in cities: an urban metabolism approach applied to Paris and Îlede-France[END_REF]. This quantity of waste consists of the inedible components of food (peelings, bones, etc.), but also of wasted food, though these could not be distinguished from each other. A rough analysis based on the work of Ademe (2019) allows to suggest a quantity of around 300 kilotonnes of wasted food, or less than 40% of household food waste, for the Île-de-France region. Household food waste is collected as mixed waste by the public service and essentially incinerated in the area. Other food waste is collected, also mixed, but comes exclusively from businesses (235 kt), and is generally put into landfill. In 2014, only 8% of solid food waste (including used cooking oil) was collected separately and recycled. This corresponds to 55 kilotonnes of food waste valorised through anaerobic digestion and composting in Île-de-France and additional 21 kilotonnes valorised outside of Île-de-France, and 28 kilotonnes of cooking oil recycled as fuel. The redistribution of unsold food from stores through food banks, a key element of the fight against waste, accounted for less than 2 kilotonnes. Although the anti-food waste campaign is intensifying, notably through food donations, such actions remain limited. All the quantities cited concern only solid food and used cooking oil, of large producers. Given the mismatch between the available data sources and the concept of food loss and waste, it is difficult to arrive at an accurate quantification of the flows. In fact, loss and waste flows are largely invisible. Nonetheless, the findings show that the great majority of food loss and waste is discharged as waste in a linear manner. While the priority should be reduction at source, most of these quantities are incinerated. Circular systems for returning organic matter from food waste to the soil are poorly developed. Because of the multiple outlets and actors responsible for them, it is difficult to measure food waste. Public statistics provide no information on the quantities of food loss and waste and the amount of wasted food that could be avoided. Moreover, the disparate and dispersed nature of this waste makes it very difficult to collect and recycle.

Identifying long-term transformations of production models

The analysis of material flows also makes it possible to objectify the long-term transformation of production models at different scales. For example, [START_REF] Domingues | Unravelling the physical, tech-nological and economic factors driving the intensification trajectories of livestock systems[END_REF][START_REF] Domingues | Exploring trade-offs among indicators of performance and environmental impact in livestock areas[END_REF] have shown that livestock farming in France has become increasingly intense over the period from 1938 to 2010. Production has doubled, rising from 0.139 Tg N/year to 0.279 Tg N/year. This increase is primarily attributable to monogastric animals (+200%) and dairy production (+180%). Greater intensity of production has been accompanied by a massive use of concentrates (cereals, cakes, co-products), which have risen from 29% to 58% of animal feed. Recent studies in animal feed have consolidated this conclusion [START_REF] Cordier | Analyse des flux de matières premières en alimentation animale en France[END_REF]. [START_REF] Domingues | Unravelling the physical, tech-nological and economic factors driving the intensification trajectories of livestock systems[END_REF] show an increase in feed imports, mainly soybean meal, and consequently a decline in self-sufficiency from 94% to 77%. Many other authors have calculated a similar order of magnitude in protein autonomy [START_REF] Jouven | Quels équilibres végé tal/animal en France métropolitaine, aux échelles nationale et petite région agricole ?[END_REF][START_REF] Cordier | Analyse des flux de matières premières en alimentation animale en France[END_REF]. This reflects the opening up of material cycles in livestock farming, which can be seen from municipal level to the national and regional scale [START_REF] Bonaudo | Analyser une transition agro-alimentaire par les flux d'azote : Aussois un cas d'étude du découplage progressif de la production et de la consommation[END_REF][START_REF] Lassaletta | Nitrogen use in the global food system: past trends and future trajectories of agronomic performance, pollution, trade, and dietary demand[END_REF][START_REF] Gameiro | Nitrogen, phosphorus and potassium accounts in the Brazilian livestock agro-industrial system[END_REF]. This average national trend hides very sharp regional disparities in the systems of flows associated with livestock production. Whereas in 1938, livestock were distributed across the whole of France, in 2010 they were concentrated in the West (50% of the national total) and in and around the Massif Central (30% of the national total) [START_REF] Domingues | Exploring trade-offs among indicators of performance and environmental impact in livestock areas[END_REF]. This dynamic has concentrated pollution. With regard to Small Agricultural Regions, [START_REF] Therond | Biens produits par l'écosystème[END_REF] estimated a disparity in livestock production of from 1.6 kg of protein/ha to more than 392 kg of protein/ha, with autonomies ranging from less than 25% to more than 100%. This method therefore makes it possible to objectify the trajectories of the agri-food system by quantifying productivity, efficiency, autonomy, losses to the environment.

Material flow analysis to identify soil contamination

Finally, the study of material flows over time can be useful in identifying soil contamination. Lead concentration in soil is a recognised indicator of anthropic pressure on the environment. Urban land thus typically has lead concentrations that are significantly higher than found in farmland. The proximity of certain industrial sites or traffic arteries may have led to significant increases in lead concentrations. The same is true of certain agronomic practices such as the spreading of urban sewage waste or sludge. This urban sewage sludge, made up of sweepings from streets, buildings and markets, was extensively used by market gardens in Paris (Larbaletrier, 1898). An examination of lead concentrations in soil clearly shows that these depend on the period of development. Developments completed before 1950 are thus observed to have the highest concentrations of ground lead. For the period after 1990, these concentrations are lower. The intermediate period reveals more varied levels of contamination. This could be explained by the mixture of soils and contamination by older soils or external deposits. The results obtained in this exploratory study [START_REF] Gitton | L'économie circulaire : cercle vertueux ou cercle vicieux ? Le cas de l'utilisation de terres maraîchères pour aménager des espaces verts urbains[END_REF] show that the sanitisation of the different areas of Paris in the 19th century entailed the evacuation of urban sewage and sludge containing pollutants, including heavy metals [START_REF] Barles | La ville délétère. Medecins et ingénieurs dans l'espace urbain. XVIII-XIXèmes siècles,Seyssel, Collection Milieux[END_REF], in proportions that are difficult to quantify, to peripheral market gardening areas. Topsoil was then returned from these same areas to the Parisian districts from which the sludge originally came, thus generating circles of a certain viciousness. An historical analysis of the flows of waste and soil thus reveals potential and actual contamination with MTE (metallic trace elements). In other words, these contaminants are associated with the reintroduction of soil contaminated by urban waste that was previously discharged into the outskirts. However, contamination from atmospheric pollution [START_REF] Foti | Trace element concentrations along a gradient of urban pressure in forest and lawn soils of the Paris region (France)[END_REF] or indeed from urban compost produced using city waste cannot be ruled out. In an extension to this initial study, we are pursuing research on the question of composts produced by gardeners themselves (on allotments and in private gardens), a subject about which very little is known. Composts produced by gardeners themselves can include green waste originating from a very long way away: for example, the green waste of Versailles is used to make compost in an allotment in Montreuil, which raises questions about local supply chains. These composts may be contaminated or are sometimes overused, and can therefore lead to re-contamination of the soil. This undermines the supposedly always virtuous principle of "recycling the city on the city". This observation prompts us to a broader scrutiny of the principles of the circular economy, especially as issues of pollution receive little attention in studies on urban metabolism.

Linking material flow analyse and participative territorial development scenarios

On the Saclay Plateau, an interest in approaches to territorial metabolism emerged in 2014, which culminated in the organisation of participatory workshops in Saclay in 2016. The workshops led to the formalisation by the researchers of three contrasting scenarios for the future of the area, scenarios which -at the end of the workshop -were presented to a panel of local actors and collectively debated [START_REF] Petit | Les Ateliers de Saclay, Réflexion en écologie territoriale sur le cas du plateau de Saclay: Synthèse[END_REF]. In this dynamic, territorial metabolism is mobilised in its analytical and operational dimension: analytical in the systemic representation of the local flow of material, and operational in the exploration of possible futures for the territory. Metabolic analysis clearly corresponds to this system of action. Among the scenarios, the most debated focuses on the re-embedding of flows of food and organic materials via rural and urban synergies. This scenario greatly increases the food supply of local agricultural systems (without aiming for complete autonomy), by closing nutrient cycles. This implies an increase in the exchange of flows between crop and animal production systems, new cultivated spaces including urban agriculture, the implementation of composting sites and the reuse of some human excreta for agricultural use.

The agri-food metabolism patterns have quantified the flows of different activities and made visible the importance of the flows corresponding to imported fertilisers and to wastewater treatment; the marginal nature of flows corresponding to local short circuits. This objectification is essential for understanding the system and thinking about the future. This work has thus evaluated the potential of different actions to reconfigure the metabolism. Certain actions appear to be limited, such as strengthening the food selfsufficiency of the territory, intensifying exchanges between cultivation and breeding livestock, while others appear more promising, such as composting or the development of urine-based fertilizers. Metabolism graphs are rarely directly used by communities. The local players recognised the education value of graphics, on condition that they are supported to take ownership of the complexity of the flow systems. Representations of metabolism make it possible to embody the action of a sector of activity, or even individual action in the general system. We are close to a form of eco-marketing whose aim is to involve the user by explaining the links between wastewater management, agro-supply and environmental impacts. These graphics have played an intermediary role between researchers and practitioners to co-design new territorial resource management organisations.

The Saclay workshops helped bring out an objective of systemic transformation of the territory. Today, it is a question of going beyond the simple development of short local circuits, to move towards the valorisation of the territory's bioresources in a circular vision with the establishment of an action research project coordinated by the multi branch agri-urban civil society organisation.

A quantitative approach to reveal the network of actors involved in a CE project

The implementation of methanisation strategies at the territorial scale requires that technological innovation be embedded in a dynamic of connection between multiple stakeholders with different interests. In the Cavigny case studied, the local authorities supply the waste, while various farmers and companies are involved in the valorisation of coproducts and the waste management. Central government departments are responsible for regulatory oversight of ICPE (classified environmental protection facilities). Finally, local people are involved with the framework consultation over the methanisation process. The coordination around issues associated with the use of inputs, the valorisation of coproducts, the management of risks, and social acceptability, require a form of innovation in organisation and governance to foster the smooth operation of facilities and generate positive outcomes for the territory. In order to understand the governance of this process, we are studying the relations between stakeholders, and the changes in those relations, over the periods of implementation of the project. Using an approach based on social networks of interactions between actors [START_REF] Ferreiro | Governance, institutions and innovation in rural territories: The case of Coruche innovation network[END_REF], we can study the relations of synergy and cooperation between stakeholders. Only recently applied to organisational approaches to territorial governance (Torre et al., 2019;[START_REF] Ferreiro | Governance, institutions and innovation in rural territories: The case of Coruche innovation network[END_REF], this method enables us to identify and quantify the types of relations that exist between stakeholders, in order to characterise their influence and observe the evolution over time of the social networks they form.

Measurement of the densities of the links between them (number of links maintained relative to the number of possible links) shows that the interacting actors develop dense relational networks. This high density reveals the importance of social capital in the interactions, which is needed to create a framework of trust and consultation that is conducive to group cohesion and a source of success for territorial renewable energy projects [START_REF] Walker | Trust and community: Exploring the meanings, contexts and dynamics of community renewable energy[END_REF]. Measurement of the centrality of the interacting stakeholders reveals that the coordination of the methanisation processes is structured around the project initiator (SMPF), which plays the role of organiser of the networks. The project initiator undertakes the mediation functions, plays the role of middleman in facilitating the sharing of flows of knowledge and collective learning, with the local arms of central government departments, the coproduct professionals and authorities are not members of the materials and energy exchange network. Nonetheless, this network and the viability of the methanisation facilities remain deficient in the use and supply of inputs, because of the measures used to prevent and reduce waste at source. While this quantitative approach to social networks enables us to identify, characterise and understand how the stakeholders in the methanisation project coordinate and govern this territorialised process, it is nevertheless difficult to apply it to spatial and territorial development issues. A theoretical framework that combines social network and proximity approaches, (Torre et al., 2019) would help to bring a better understanding.

Discussion

Advantages and practical limitations of studies of territorial metabolisms and their governance

The metabolic approach has numerous advantages, the first being a systemic vision of agri-food systems, in which the flow of materials is identified for the different sectors of activity, from agricultural production through to processing and distribution, consumption and waste management. This approach reveals the flows -and the losses and waste -entailed in different activities, their causes and their scales. Metabolic analysis is therefore a way to formalise the link between technical systems, activities and systems of flows, with the positive and negative effects of circularity. The analysis makes it possible to situate activities and flows of materials in connection with a given territory but also the linear or circular dimensions of the metabolism. This encourages the analysis of processes internal or external to the territory, together with changes of scale. This approach also makes it possible to decipher the network of actors and the governance of these flows. Finally, this approach allows us to identify priorities for action or different scenarios. It can cast light on the tension between two objectives of territorial development to reconcile frugality and circularity [START_REF] Ewijk | Limitations of the waste hierarchy for achieving absolute reductions in material throughput[END_REF].

Nonetheless, the results emphasise the difficulty of quantifying and situating flows, in particular food flows, given the mismatch between the concepts and the available data sources. Developing a circular economy, i.e. managing flows of materials, demands a better characterisation of the diversity of bioresources, of their variation in time and space, but also attention to changes in scale and to levels of efficiency. These major advances require the application of tools and methods from geography, from economics, from sociology (GIS, territorial LCA, dynamic spatial modelling, network analysis, …).

Unexpected effects of circularity at the territorial level

Just as the implementation of circularity principles at the micro level of a company or a sector can have negative effects at the macro-economic level (notably through "rebound" effects), the effect of the implementation of circularity at the territorial level can only have a weak impact at the global level, by not sufficiently questioning the system as a whole. Circularity in itself is not a guarantee of the sustainability and its implementation at local scales can have unforeseen perverse effects. If we want to drive an ecological transition, we must be able to reason about the consequences of changes from one scale to another, the direct and indirect effects, local, distant and global effects. This imposes to considering of the changes of scales and territorial impacts [START_REF] Loiseau | Territorial Life Cycle Assessment (LCA): what exactly is it about? A proposal towards using a common terminology and a research agenda[END_REF].

The valorisation of waste as a resource is a way to extract profit from it and to incorporate an initially undesirable flow into an economic process to create value. Encouraged by CE, this strategy can be a source of tension [START_REF] Redlingshöfer | Are waste hierarchies effective in reducing environmental impacts from food waste? A systematic review for OECD countries[END_REF]. Numerous authors have noted how the narrative of "waste as a resource" suggests that there will be no more limits on the consumption of resources and on the production of waste (since this is recycled) [START_REF] Bradshaw | Waste Law and the Value of Food[END_REF]. This evades the main question of waste reduction at source, and hence the intensity of consumption. Yet the best strategy for handling waste is not to produce it in the first place, which can run counter to an economic choice if waste has a value, notably as a result of subsidies or public policies. Furthermore, food waste recycling practices have the potential to generate contamination (packaging residues, physical or chemical of recycling sewage sludge). This raises the question of risk assessment for the different recycling industries. There are only a few voices speaking up about the problem of contamination [START_REF] Baxter | Contaminated interaction: another barrier to circular material flows[END_REF] and about the need to remove dangerous waste from the loop.

Complex system, diversity and resilience

Closing loops presents numerous sociotechnical, organisational and economic obstacles, which are reflected in production and/or supply risks. Many bioresources are highly disparate, dispersed and nonstandardised. The density of activities and flows poses problems in terms of the attainment of critical volume and viable distances of exchange. In these circumstances, the match between supply and demand is hard to achieve through the market or through contracts between actors [START_REF] Dourmad | Les productions animales dans la bioéconomie. In : Numéro spécial. De grands défis et des solutions pour l'élevage[END_REF]. The procedures and forms of management must be adapted to different territorial contexts (small flows with a dispersed network versus mass flows with a centralised network). Other obstacles can be cited such as the limited resources of the actors (labour, time…), the lack of technical references and know-how, logistical and investment costs, regulatory constraints, social acceptability. All forms of coordination should be underpinned by the assessment of local potential for the production and recycling of bioresources and local demands. The territorial stakeholders will need to introduce adaptive management to deal with the variations and disparities in the flows. Trust between actor, long term engagement and the presence of a coordinating actor, which takes decisions but also assumes a large proportion of the risks, is an important factor for success (Ruth and Davidsdottir, 2009;[START_REF] Chopra | Understanding resilience in industrial symbiosis networks: Insights from network analysis[END_REF].

The analysis of resilience of the agri-food system is essential [START_REF] Walker | Resilience, adaptability and transformability in social-ecological systems[END_REF][START_REF] Derissen | The relationship between resilience and sustainability of ecological economic systems[END_REF]. For instance, [START_REF] Fraccascia | Rethinking Resilience in Industrial Symbiosis: Conceptualization and Measurements[END_REF] suggest incorporating in the analysis two main vectors of resilience in industrial symbioses: diversity of enterprises and networks, and ubiquity of waste. This entails looking at the ubiquity of flows and the specialisation of functions (companies) in metabolisms. Thought must also be given to the (functional) redundancy of these flows and of the functions. Indeed, flows of materials must come from several sources (security of supply) and must potentially have several uses (security of outputs). In the same way, a stakeholder/company can perform several functions in the system of flows and functions must be fulfilled by several stakeholders/companies. This is about conceiving the system of flows and actors as an ecosystem with a base level of functional diversity and redundancy. Closing loops is often synonymous with diversifying production, creating new activities and managing waste. This means managing more and smaller flow exchanges. It is therefore a question of managing more complex systems, which implies higher learning, investment and even management costs.

Conclusion

The transformation of agri-food systems plays an essential role in the ecological transition. These systems drive a number of territorial innovations, both for the production of bioproducts and for waste management, including the limitation of environmental losses and impacts. Circular frameworks, such as social or territorial metabolism, industrial and territorial ecology, have led to a rethink about the notion of resources, wastes and their valorisation, with an emphasis on their limitations and the gridlock of linear systems. They emphasise the possibilities inherent in innovative partnerships designed to reinforce the reterritorialisation of activities and circular approaches. Based on contrasting case studies, we seek to identify the benefits and challenges of the circular economy for analysing and steering interaction between agri-food systems and territorial development. The advantages of material flow analyses are to objectify the importance of certain flows, to make them visible when information is nonexistent and to highlight long-term transformations in agri-food systems. Our article also identifies numerous sociotechnical, organisational and economic challenges that need to be tackled in order to close the loops in the circular economy. The challenges concern in particular the definition of an appropriate territory of action, the design and management of these complex systems of flows, the resilience of these systems with the management of risks, and finally the main question of reducing the intensity of material flow.

Figure 1 :

 1 Figure 1: Specific positioning of our five case studies in an agri-food system The number represents the sub-sectors analysed by the five case studies: 1/ Interactions between livestock farming and territories, 2/ Evaluation of food loss and waste, 3/ Analysis of soil flows in urban green spaces, 4/ Governance of methanisation projects, 5/ Material flow representations for territorial development. Studies 1, 2, 3 and 5 focus on the quantification and analysis of flows entering and leaving the sub-sectors concerned, while study 4 focuses on the analysis of the governance of flows in the methanisation sub-sector.

This text is the result of the work of the "Industrial and territorial ecology, circular economy" cross-disciplinary group within UMR SADAPT between

and 2022.


Tran Thi

Hong Hanh

Matej Martinc

Antoine Doucet

Senja Pollak

Hanh Thi

Hong Tran

Can Cross-domain Term Extraction Benefit from Cross-lingual Transfer?

Keywords: Term extraction, XLM-RoBERTa, Sequence labeling, Cross-lingual, Cross-domain

published or not. The documents may come   L'archive ouverte pluridisciplinaire

Introduction

Terms are textual expressions that denote concepts in a specific field of expertise. They are beneficial for several terminographical tasks performed by linguists (e.g., construction of specialized term dictionaries [START_REF] Serrec | Automating the compilation of specialized dictionaries: Use and analysis of term extraction and lexical alignment[END_REF]). Moreover, terms can also support and improve several complex downstream natural language processing (NLP) tasks, such as topic detection [START_REF] Elkishky | Scalable topical phrase mining from text corpora[END_REF], information retrieval [START_REF] Lingpeng | Improving retrieval effectiveness by using key terms in top retrieved documents[END_REF], machine translation [START_REF] Wolf | From statistical term extraction to hybrid machine translation[END_REF], etc. Automatic term extraction (ATE) was born to ease the time and effort needed to manually identify terms from domain-specific corpora.

The TermEval 2020 shared task on monolingual ATE, organized as part of the CompuTerm workshop [START_REF] Rigouts Terryn | TermEval 2020: Shared Task on Automatic Term Extraction Using the Annotated Corpora for Term Extraction Research (ACTER) Dataset[END_REF], presented one of the first opportunities to systematically study and compare various ATE systems with the introduction of a new annotated corpus that covers four domains in three languages: The Annotated Corpora for Term Extraction Research (ACTER) dataset [START_REF] Rigouts Terryn | TermEval 2020: Shared Task on Automatic Term Extraction Using the Annotated Corpora for Term Extraction Research (ACTER) Dataset[END_REF][START_REF] Rigouts Terryn | In no uncertain terms: a dataset for monolingual and multilingual automatic term extraction from comparable corpora[END_REF]. While the workshop was an important step forward in systematic comparison, the lessresourced languages (e.g., Slovenian) have not yet been sufficiently explored and remain a research gap. Furthermore, there is still room for improvement in performance and replicability as the open-sourced code is often not available.

Inspired by the success of Transformer-based models in the TermEval 2020 competition [START_REF] Hazem | TermEval 2020: TALN-LS2N System for Automatic Term Extraction[END_REF] and the rise of cross-lingual learning [START_REF] Lang | Transforming term extraction: Transformer-based approaches to multilingual term extraction across domains[END_REF], we propose to explore the performance of the multilingual XLM-RoBERTa pretrained model [START_REF] Conneau | Unsupervised cross-lingual representation learning at scale[END_REF] in a multilingual setting, and in a cross-lingual setting, where the model is fine-tuned on several languages and tested on a new unseen language. We model the ATE as a sequence-labeling task. Sequence-labeling approaches have been successfully applied to a range of similar NLP tasks, including Named Entity Recognition [START_REF] Lample | Neural Architectures for Named Entity Recognition[END_REF][START_REF] Hanh | Named entity recognition architecture combining contextual and global[END_REF] and Keyword Extraction [START_REF] Martinc | Tnt-kid: Transformer-based neural tagger for keyword identification[END_REF][START_REF] Koloski | Out of thin air: Is zeroshot cross-lingual keyword detection better than unsupervised?[END_REF]. The experiments are conducted in the cross-domain setting on the ACTER dataset containing texts in four domains (Corruption, Wind energy, Equitation, and Heart failure) with three languages (English, French, and Dutch) and the RSDO5 corpus4 [START_REF] Jemec Tomazin | Corpus of term-annotated texts RSDO5 1[END_REF] containing Slovenian texts from four domains (Biomechanics, Chemistry, Veterinary, and Linguistics).

The main contributions of this paper can be summarized as follows:

-We systematically evaluate the performance of XLM-RoBERTa language model on the cross-domain term extraction task on two datasets covering English, French, Dutch, and a less-resourced language, Slovenian. -We compare the performance of cross-and multilingual toward monolingual approaches to determine the general applicability of multilingual language models for sequence labeling in both rich-and less-resourced languages, for which manually labeled training resources are and are not available.

This paper is organized as follows: Section 2 presents the related work. Next, we introduce the dataset, methodology, experimental details as well as evaluation metrics in Section 3. The results with error analysis are discussed in Section 4 and 5 before we conclude and present future works in Section 6.

Related Work

The history of ATE has its beginnings during the 1990s with research done by Damerau et al. [START_REF] Damerau | Evaluating computer-generated domain-oriented vocabularies[END_REF] , Justeson et al. [START_REF] Justeson | Technical Terminology: Some Linguistic Properties and an Algorithm for Identification in Text[END_REF]. ATE systems usually employ the twostep procedure: (1) extracting a list of candidate terms; and (2) determining which candidate terms are correct using supervised or unsupervised techniques. We divide these techniques into the approaches based on (1) term characteristics and (2) machine learning and deep learning.

Approaches based on term characteristics

Traditional ATE approaches relied on linguistic knowledge and distinctive linguistic aspects of terms to extract possible candidates. Several NLP tools (e.g., tokenization, lemmatization, stemming, chunking, PoS tagging, etc.) are employed to obtain linguistic profiles of term candidates. As a heavily languagedependent approach, the better the quality of the pre-processing tools (e.g., FLAIR [START_REF] Akbik | Flair: An easy-to-use framework for state-of-the-art nlp[END_REF], Stanza [START_REF] Qi | Stanza: A python natural language processing toolkit for many human languages[END_REF]), the better the quality of linguistic ATE methods. More recently, several studies were proposed that preferred the statistical approach toward ATE. The most common statistical approach relies on the assumption that a higher candidate term frequency in a domain-specific corpus implies a higher likelihood that a candidate is an actual term. Some measures relying on this assumption include termhood [START_REF] Vintar | Bilingual Term Recognition Revisited: The Bag-of-equivalents Term Alignment Approach and its Evaluation[END_REF], unithood [START_REF] Daille | Towards Automatic Extraction of Monolingual and Bilingual Terminology[END_REF] or C-value [START_REF] Frantzi | The c-value/nc-value method of automatic recognition for multi-word terms[END_REF]. More popular statistical approaches take also into account the frequency of the term internal words compared to the term frequency (e.g., Mutual Information) to identify rare terms and remove frequent words. Many current systems still apply this approach's variation, most commonly in hybrid systems combining linguistic and statistical information [START_REF] Kessler | Extraction of terminology in the field of construction[END_REF][START_REF] Repar | TermEnsembler: An Ensemble Learning Approach to Bilingual Term Extraction and Alignment[END_REF].

Approaches based on machine learning and deep learning

Recently, advances in embeddings and deep neural networks have also influenced the field of term extraction. Several embeddings have been investigated for the task at hand, for example, uni-gram term representations constructed from a combination of local and global vectors [START_REF] Amjadian | Local-Global Vectors to Improve Unigram Terminology Extraction[END_REF], non-contextual [START_REF] Zhang | Semre-rank: Improving automatic term extraction by incorporating semantic relatedness with personalised pagerank[END_REF], contextual [START_REF] Kucza | Term Extraction via Neural Sequence Labeling a Comparative Evaluation of Strategies Using Recurrent Neural Networks[END_REF] word embeddings, and the combination of both [START_REF] Gao | Feature-less End-to-end Nested Term extraction[END_REF]. The first use of language models for the ATE task is documented in the TermEval 2020 [START_REF] Rigouts Terryn | TermEval 2020: Shared Task on Automatic Term Extraction Using the Annotated Corpora for Term Extraction Research (ACTER) Dataset[END_REF] competition on the ACTER dataset, a collection of four domain-specific corpora in three languages (English, French, and Dutch). There, the winning approach on the Dutch corpus used pretrained GloVe word embeddings fed into a BiLSTM-based neural architecture. Meanwhile, the winning approach on the English corpus [START_REF] Hazem | TermEval 2020: TALN-LS2N System for Automatic Term Extraction[END_REF] relied on the extraction of all possible n-gram combinations, which are fed into a BERT binary classifier that determines for each n-gram inside a sentence, whether it is a term or not. Besides, several variations of Transformer-based models have also been investigated (e.g., RoBERTa and CamemBERT have also been used in the TermEval 2020 [START_REF] Hazem | TermEval 2020: TALN-LS2N System for Automatic Term Extraction[END_REF] challenge). Further work inspired by TermEval 2020 includes the HAMLET [START_REF] Rigouts Terryn | HAMLET: Hybrid Adaptable Machine Learning approach to Extract Terminology[END_REF], which proposes a hybrid adaptable machine learning approach that combines the linguistic and statistical clues to detect terms.

When it comes to more general related work applicable to ATE task, the research by Kucza et al. [START_REF] Kucza | Term Extraction via Neural Sequence Labeling a Comparative Evaluation of Strategies Using Recurrent Neural Networks[END_REF] was one of the first to propose to model term extraction as a sequence labeling task. Cross-lingual sequence labeling was, on the other hand, explored in Conneau et al. [START_REF] Conneau | Unsupervised cross-lingual representation learning at scale[END_REF] and Lang et al. [START_REF] Lang | Transforming term extraction: Transformer-based approaches to multilingual term extraction across domains[END_REF], who take advantage of XLM-RoBERTa, the model we also employ in this work, to compare three cross-lingual approaches, including a binary sequence classifier, a sequence classifier, and a token classifier on several sequence-labeling tasks. Finally, Lang et al. [START_REF] Lang | Transforming term extraction: Transformer-based approaches to multilingual term extraction across domains[END_REF] further proposes to use a multilingual encoder-decoder denoising pretraining model called mBART [START_REF] Liu | Multilingual denoising pre-training for neural machine translation[END_REF] to generate sequences of comma-separated terms from the input. The results demonstrate the capability of multilingual models to outperform monolingual ones in some specific scenarios and the potential of cross-lingual learning.

Approaches for Slovenian term extraction

When it comes to the ATE for Slovenian, and more generally to less-resourced languages, the research is still hindered by the lack of gold standard corpora and limited use of neural methods. The things are nevertheless slowly improving. For example, in recent years, Slovenian KAS corpus was compiled [START_REF] Erjavec | The kas corpus of slovenian academic writing[END_REF]. The release was quickly followed by another corpus designed for term extraction, the RSDO5 corpus that we use in our study [START_REF] Jemec Tomazin | Corpus of term-annotated texts rsdo5 1[END_REF]. Regarding the employment of ATE models for Slovenian, one of the first approaches was the statistical approach by Vintar et al. [START_REF] Vintar | Bilingual Term Recognition Revisited: The Bag-of-equivalents Term Alignment Approach and its Evaluation[END_REF]. The SOTA was proposed by Ljubevsic et al. [START_REF] Ljubešić | Kas-term: Extracting Slovene Terms from Doctoral Theses via Supervised Machine Learning[END_REF], where they extract the initial candidate terms using the CollTerm tool [START_REF] Pinnis | Extracting data from comparable corpora[END_REF], a rule-based system employing a complex language-specific set of term patterns (e.g., POS tag,...) from the Slovenian SketchEngine module [START_REF] Fišer | Terminology extraction for academic slovene using sketch engine[END_REF], followed by a machine learning classification approach with features representing statistical term extraction measures. Another recent approach by Repar et al. [START_REF] Repar | TermEnsembler: An Ensemble Learning Approach to Bilingual Term Extraction and Alignment[END_REF] focuses on term extraction and alignment, where the main novelty is in using an evolutionary algorithm for the alignment of terms. On the other hand, the deep neural approaches have not been explored for Slovenian yet. Another problem is the open-sourced code is often not available for most current benchmark systems, hindering their reproducibility (for Slovenian, only the code from Ljubevsic et al.'s method [START_REF] Ljubešić | Kas-term: Extracting Slovene Terms from Doctoral Theses via Supervised Machine Learning[END_REF] is available). In our own work [START_REF] Tran | A transformer-based sequencelabeling approach to the slovenian cross-domain automatic term extraction[END_REF], we also implemented the Transformers-based sequence labeling approach that we extend in this study in a cross-lingual and multilingual evaluation.

Methodology

Section 3.1 presents our chosen datasets with a brief description of the structure, term frequency, and label distribution. We describe the general methodology, experimental setup, and the implementation details in Section 3.2 and 3.3. Finally, in Section 3.4 we describe the chosen evaluation metrics for the ATE task.

Dataset

The experiments were conducted on two datasets (ACTER [START_REF] Rigouts Terryn | TermEval 2020: Shared Task on Automatic Term Extraction Using the Annotated Corpora for Term Extraction Research (ACTER) Dataset[END_REF] and RSDO5 version 1.1 [START_REF] Jemec Tomazin | Corpus of term-annotated texts RSDO5 1[END_REF]) containing texts from different languages and domains. The structures of both datasets are presented in Figure 1. The ACTER dataset is a collection of 12 corpora covering four domains (Corruption (corp), Dressage (equi), Wind energy (wind), and Heart failure (htfl)) in three languages (English (en), French (fr) and Dutch (nl)). The dataset has two types of gold standard annotations: one including both terms and named entities (NES), and the other one containing only terms (ANN). Table 1 summarizes the number of documents and unique terms for each domain. Note the discrepancy in size between the Heart failure domain and the other three domains, with the Heart failure domain containing the much more unique terms and documents The second dataset is the RSDO5 corpus version 1.1 [START_REF] Jemec Tomazin | Corpus of term-annotated texts RSDO5 1[END_REF] containing texts in Slovenian (sl), a less-resourced Slavic language with rich morphology. Compiled during the course of the RSDO6 national project, the RSDO5 corpus contains 12 documents (including three Ph.D. theses, a scientific book based on a Ph.D.

thesis, four graduate level textbooks, and four journal articles) with altogether about 250,000 words collected from diverse sources between 2000 to 2019 covering domains of Biomechanics (bim), Chemistry (kem), Veterinary (vet), and Linguistics (ling). The numbers of documents, tokens, and unique terms per domain are reported in Table 2. The documents from the Linguistics and Veterinary domains are longer (i.e., they have more tokens) and also contain more terms than Biomechanics and Chemistry. Most terms are made of one up to three words and only a few terms are longer than seven words. An example of a long term found in the corpus would be "stojo po obračanju v nasprotni smeri urinega kazalca" (stand after turning counterclockwise) in Biomechanics. Furthermore, both datasets contain several nested terms, i.e., a shorter term may appear within a larger term and vice versa. For example, in the RSDO5's Biomechanics domain, the term "navor" (torque) appears in terms such as "sunek navora" (torque shock), "zunanji sunek navora" (external torque shock), and "izokinetični navor" (isokinetic torque); in ACTER's English Corruption, term "confiscation" appears also in terms such as "confiscation of corruption proceeds", "confiscation of criminal assets", and "confiscation of the proceeds of crime", to mention a few. This makes the labeling harder and the classifier needs to infer from the context whether a specific term is part of a longer term.

Methodology

We experiment with XLM-RoBERTa, a Transformer-based model pre-trained on 2.5TB of filtered CommonCrawl data containing 100 languages. We consider ATE as a sequence-labeling task where the model returns a label for each token in a text sequence using the (B-I-O) labeling regime [START_REF] Rigouts Terryn | HAMLET: Hybrid Adaptable Machine Learning approach to Extract Terminology[END_REF][START_REF] Lang | Transforming term extraction: Transformer-based approaches to multilingual term extraction across domains[END_REF]. Here, B stands for the beginning word in the term, I stands for the word inside the term, and O stands for the word not part of the term. The terms from a gold standard list are first mapped to the tokens in the raw text and each word inside the text sequence is annotated with one of three labels (see examples in Fig. 2). The model is first trained to predict a label for each token in the input text sequence (e.g., we model the task as token classification) and then applied to the unseen text (test data). Finally, from the tokens or token sequences labeled as terms, the final candidate term list for the test data is composed.

We evaluate the cross-domain performance of the model in a monolingual, cross-lingual, and multilingual setting. Altogether, 55 different scenarios are tested. The distinct settings are described below.

1. Monolingual setup. We evaluate how well the model performs when there is a language-specific training corpus available and there is a match between the language of the train set and the language of the test set. We fine-tune our model in a single language, which means we train three monolingual models for three languages (English, French, Dutch) and test each model in the same language as well as 12 monolingual models for Slovenian given 12 different combinations of train-validation-test split regarding the domains. This can be considered as a baseline to which we compare other settings. 2. Cross-lingual setup. We evaluate the capability of the model to apply the knowledge learned about ATE in one or more languages for ATE in another unseen language. Therefore, we fine-tune the ATE model in one or more languages (e.g., English and Dutch) and test it on another language not appearing in the train set (e.g., French). In this scenario, we, therefore, examine how well the model performs without the language-specific training corpus and how good the knowledge transfer between different languages is. 3. Multilingual setup. We fine-tune our model using a.) training datasets from all languages in the ACTER dataset (English, French, and Dutch) or using b.) training datasets from all languages in the ACTER dataset plus the Slovenian training dataset from the RSDO5 corpus, and then apply the model to the test sets of all languages. By doing so, we examine whether adding more data from other languages to the train set that matches the target language improves the predictive performance of the model.

All three settings are applied in a cross-domain evaluation scenario, where we use two domains for training, another domain for validation, and the rest for testing except the multilingual setting with additional Slovenian corpus in the training set where we use two domains from ACTER and all domains from RSDO5 corpus for the training. This way we want to check the generalization capabilities of the model, i.e. whether the knowledge the model obtained on one domain can be applied to the new, unseen domains, which would make the model applicable to arbitrary domains and therefore much more useful. In the ACTER dataset, we use Corruption and Wind energy domains as parts of the training, Equitation domains for validation, and Heart failure domain for testing in order to allow for a direct comparison with other benchmarks sharing the same train-validation-test setting [START_REF] Lang | Transforming term extraction: Transformer-based approaches to multilingual term extraction across domains[END_REF], using the same dataset and evaluation setting (predicting on Heart Failure test set) from the related work. Meanwhile, in the RSDO5 corpus, we explore different train-validation-test combinations.

We divide the dataset into train-validation-test splits. The train split is used for fine-tuning the models while the validation split is used to prevent over-fitting during the fine-tuning phase. Finally, the test split is used for evaluation and is excluded during the model training. The model is fine-tuned on the training set to predict the probability for each word in a word sequence whether it is a part of the term (B, I) or not (O). An additional token classification head containing a feed-forward layer with a softmax activation is added on top of each model.

Implementation Details

We consider ATE as a sequence-labeling task and the models are trained to predict the labels from the (B-I-O) annotation scheme. The distribution across label types and the proportion of (B) and (I) labels in the total number of tokens per domain and per language are presented in Table 3. In the ACTER dataset, the proportion of terms in the texts is the largest for English, followed by French and then Dutch. The proportion of terms increases by from 1% upto 5% when adding NEs into the gold standards. In both datasets, the number of tokens annotated as terms (or parts of the term) only represents up to one-fourth (but usually much less) of the total tokens in the corpus, which means there is a significant imbalance between (B, I) tokens and tokens labeled as not terms (O). We employ the XLM-RoBERTa token classification model and its "fast" XLM-RoBERTa tokenizer from the Huggingface library 7 . We fine-tune the model for up to 20 epochs (i.e., we employ the early stopping regime) using the learning rate of 2e-05, training and evaluation batch size of 32, and sequence length of 512 tokens, since this hyperparameter configuration performed the best on the validation set. The documents are first split into sentences. Then, the sentences containing more than 512 tokens are truncated, while the sentences with less than 512 tokens are padded with a special < P AD > token at the end. During finetuning, the model is evaluated on the validation set after each training epoch, and the best-performing model is applied to the test set. The model predicts each word in a word sequence whether it is a part of a term (B, I) or not (O).

The sequences identified as terms are extracted from the text and put into a set of all predicted candidate terms. A post-processing step to lowercase all the candidate terms is applied before we compare our derived candidate list with the gold standard.

Evaluation Metrics

We evaluate the performance of the ATE system by comparing the candidate list extracted on the whole test set level with the manually annotated gold standard of each domain using strictly matching with Precision (P), Recall (R), and F1score (F1). These evaluation metrics have also been used in the related work, including the TermEval 2020 [START_REF] Hazem | TermEval 2020: TALN-LS2N System for Automatic Term Extraction[END_REF][START_REF] Rigouts Terryn | TermEval 2020: Shared Task on Automatic Term Extraction Using the Annotated Corpora for Term Extraction Research (ACTER) Dataset[END_REF][START_REF] Lang | Transforming term extraction: Transformer-based approaches to multilingual term extraction across domains[END_REF] and Slovenian benchmark [START_REF] Ljubešić | Kas-term: Extracting Slovene Terms from Doctoral Theses via Supervised Machine Learning[END_REF]. Therefore, our results are directly comparable to the SOTA methods.

Results

In this Section, we determine the predictive power of monolingual, cross-lingual, and multilingual learning in ACTER and RSDO5 test sets as well as compare the results from our proposed approaches to the SOTAs from the related work.

Prediction on the ACTER test set

Table 4 demonstrates the performance of XLM-RoBERTa on the cross-domain sequence-labeling ATE task on the ACTER test set in the monolingual, crosslingual, and multilingual setting. We group the results according to the test language in the ACTER corpora for better comparison among settings. The results indicate that cross-and multilingual models surpass the performance of the monolingual ones according to all evaluation metrics except for when it comes to the Precision obtained by the French monolingual model on the French test set. Multilingual models tend to outperform cross-lingual ones, except for the cross-lingual model trained in Dutch and applied to the English test set. This multilingual model boosts the F1-score performance by up to 2% in ANN and 1% in the NES task when compared to the second-highest-performing model. By adding the Slovenian corpus with four different domains into the training set, the multilingual model demonstrates a significant improvement in Recall across all test languages, which, on average, increases by 18.17% in ANN and 13.54% in NES test set compared with the monolingual setting.

Table 5 presents a comparison between the best-performing models in this work in terms of F1-score and the benchmark approaches in the ACTER dataset, including the solutions from the winning teams in the competition (TALN-LS2N [START_REF] Hazem | TermEval 2020: TALN-LS2N System for Automatic Term Extraction[END_REF] won on the English and French test set while NLPLab UQAM [START_REF] Le | Multilingual automatic term extraction in low-resource domains[END_REF] won on the Dutch test set) and other methods proposed in Rigouts et al. [START_REF] Rigouts Terryn | HAMLET: Hybrid Adaptable Machine Learning approach to Extract Terminology[END_REF] and Lang et al. [START_REF] Lang | Transforming term extraction: Transformer-based approaches to multilingual term extraction across domains[END_REF], which are described in Section 2. Note that all the approaches from the related work are cross-domain and use the Heart failure domain as the test set and the rest of the data for training or validation. For the ANN task in English and Dutch and the NES task in French, our methods outperform other approaches in terms of F1-score. Despite not surpassing the SOTA in the French ANN task and the other two NES tasks, our method still offers competitive performance being outperformed by the HAMLET approach [START_REF] Rigouts Terryn | HAMLET: Hybrid Adaptable Machine Learning approach to Extract Terminology[END_REF] with a small margin of 0.39% in ANN French, and by the token classifier [START_REF] Lang | Transforming term extraction: Transformer-based approaches to multilingual term extraction across domains[END_REF] with about 0.33% in NES English. In terms of multilingual evaluation, we show that in contrast to the findings of Lang et al. [START_REF] Lang | Transforming term extraction: Transformer-based approaches to multilingual term extraction across domains[END_REF], adding different languages in general slightly improves the models.

Table 5: F1-score comparison between our results and related work in ACTER.

Methods

English French Dutch ANN NES ANN NES ANN NES

Winning teams [START_REF] Hazem | TermEval 2020: TALN-LS2N System for Automatic Term Extraction[END_REF] 44.99 46.66 45.94 48.15 18.60 18.70 HAMLET [START_REF] Rigouts Terryn | HAMLET: Hybrid Adaptable Machine Learning approach to Extract Terminology[END_REF] 54.20 55.40 60.20 60.80 66.10 66.00 Sequence Classifier [START_REF] Lang | Transforming term extraction: Transformer-based approaches to multilingual term extraction across domains[END_REF] x 46.00 x 48.10 x 58.00 NMT [START_REF] Lang | Transforming term extraction: Transformer-based approaches to multilingual term extraction across domains[END_REF] x 55.30 x 57.60 x 59.60 Token classifer [START_REF] Lang | Transforming term extraction: Transformer-based approaches to multilingual term extraction across domains[END_REF] x

Evaluation on the RSDO5 test set

We also apply monolingual and multilingual cross-domain approaches to the Slovenian RSDO5 dataset. The results grouped by the test domain are presented in Table 6. The monolingual approach, where we use two domains from the RSDO5 corpus for training, validate on the third domain, and test on the last domain, proves to have relatively consistent performance across all the combinations, achieving Precision of more than 62%, Recall of no less than 55%, and F1-score above 61%. The model performs slightly better for the Linguistics and Veterinary domains than for Biomechanics and Chemistry. The difference in the number of terms and length of terms per domain pointed out in Section 3.1 might be one of the factors that contribute to this behavior. Moreover, a significant performance boost can be observed for the Linguistics domain when the model is trained in the Chemistry and Veterinary domains, and for the Vet-erinary domain, when the model is trained in Biomechanics and Linguistics. In these two settings, the model achieves an F1-score of more than 68%. We also explore the performance of multilingual approaches on the RSDO5 test sets. We train the model using the ANN and NES labels from all domains of the ACTER dataset and on two domains from the RSDO5 dataset, validate on the third RSDO5 domain, and test on the last domain. Table 6 demonstrates the comparative performance of the multilingual and the monolingual approaches, which is consistent with the results in the prediction of the ACTER test set. Furthermore, in Table 7, we present the results from the related work for the RSDO5 dataset [START_REF] Ljubešić | Kas-term: Extracting Slovene Terms from Doctoral Theses via Supervised Machine Learning[END_REF] in comparison to the proposed monolingual and multilingual approaches. The results from [START_REF] Ljubešić | Kas-term: Extracting Slovene Terms from Doctoral Theses via Supervised Machine Learning[END_REF]'s method are taken from Hanh et al. [START_REF] Tran | A transformer-based sequencelabeling approach to the slovenian cross-domain automatic term extraction[END_REF]. In general, our approach outperforms the approach proposed in Ljubevsic et al. [START_REF] Ljubešić | Kas-term: Extracting Slovene Terms from Doctoral Theses via Supervised Machine Learning[END_REF] by a large margin on all domains and according to all evaluation metrics, especially when it comes to Recall. Overall, we achieve results roughly twice as high as the approach proposed by Ljubevsic et al. [START_REF] Ljubešić | Kas-term: Extracting Slovene Terms from Doctoral Theses via Supervised Machine Learning[END_REF] in terms of F1-score for all test domains regarding both monolingual and multilingual learning. We show that the multilingual experiments do in several cases improve our monolingual results [START_REF] Tran | A transformer-based sequencelabeling approach to the slovenian cross-domain automatic term extraction[END_REF], but this is not systematic.

Error analysis

In order to determine whether the term length affects the models' performance, we calculate Precision and Recall separately for terms of length k = {1,2,3,4, ≥5}. The number of predicted candidate terms (Preds), ground truth (GT), correct predictions (TPs), Precision, and Recall regarding different term lengths k and test domains are presented in Table 8. The results for ACTER's dataset (Table 8a) were obtained by employing the best performing model for a specific language in terms of F1-score on the Heart failure test set. The results for the RSDO5 dataset (Table 8b) were obtained by employing the best-performing model for a specific test domain in F1-score. The models proved to be good at predicting terms containing up to four words for English and Dutch and up to three words for French. The results on the RSDO5 dataset are similar, showing that the models are good at predicting short terms containing up to three words for all four domains of the RSDO5 corpus. The best model applied to the Linguistics test domain also shows relatively good performance when it comes to the prediction of longer terms, achieving 75.00% Precision and a decent 31.03% Recall for terms with at least five words. Despite the relatively high Precision for prediction of long terms in the Veterinary and Biomechanics test domains, the Recall is pretty low, most likely due to the small amount of longer terms in the dataset on which the models are trained. When it comes to predictions in the Chemistry domain, there are no correct term predictions that consist of more than five words.

Conclusion

In summary, we investigated the possibilities of cross-and multilingual learning compared to the monolingual setting in the cross-domain sequence-labeling term extraction given the experiments conducted on multi-domain corpora, namely the ACTER and RSDO5 datasets. We also evaluated the impact of cross-and multilingual models on the ACTER corpora only and by further adding the texts from the Slovenian RSDO5 corpus in the training set. In addition, we examined the cross-lingual effect of rich-resourced training language on lessresourced testing one such as Slovenian. The results demonstrate a promising impact of multilingual and cross-lingual cross-domain learning that outperforms the related works in both datasets, which proves their potential when transferring from the rich-to the less-resourced languages.

However, we believe that there remains room for improvement in the field of supervised term extraction. In the future, we suggest the integration of active learning into our current approach to improve the output of the automated method by dynamical adaptation after human feedback. By learning with humans in the loop, we aim at getting the most information with the least amount of term labels. We will also evaluate the contribution of active learning in reducing the annotation effort and determine the robustness of the incremental active learning framework across different languages and domains.

Fig. 1 :

 1 Fig. 1: The structure of RSDO5 and ACTER regarding languages and domains.

Fig. 2 :

 2 Fig. 2: A sample of our labels in the RSDO5 corpus for term extraction.

Table 1 :

 1 Number of documents and unique terms in the ACTER dataset.

		Corruption (corp) Equitation (equi) Wind energy (wind) Heart failure (htfl)
	Languages	Docs	Terms ANN NES	Docs	Terms ANN NES	Docs	Terms ANN NES	Docs	Terms ANN NES
	en	19	927 1,173	34 1,155 1,575	5	1,091 1,534	190 2,361 2,585
	fr	19	979 1,207	78	961 1,181	2	773	968	210 2,228 2,374
	nl	12 1,047 1,295	65 1,393 1,544	8	940	1,245	174 2,074 2,254

5

.

Table 2 :

 2 Number of documents, tokens, and unique terms in the RSDO5 dataset.

	Biomechanics (bim) Chemistry (kem)	Veterinary (vet)	Linguistics (ling)
	Docs Tokens Terms Docs Tokens Terms Docs Tokens Terms Docs Tokens Terms
	3	61,344 2,319	3	65,012 2,409	3	75,182 4,748	3 109,050 4,601

Table 3 :

 3 Label distribution and proportion of terms appearing per domain.

	Languages	Corruption (corp) B I O % Term B	Equitation (equi) I O % Term B Wind energy (wind) I O % Term	Heart failure (htfl) B I O % Term
	ANN en 4,558 2,200 44,287 13.24 10,745 1,938 46,215 21.53 5,046 3,323 49,873 14.37	9,819 4,504 41,522 25.65
	ANN fr 4,461 2,823 51,918 12.30	8,420 2,373 50,487 17.61 5,928 4,405 43,976 19.03	7,165 4,027 43,976 20.29
	ANN nl 4,251 1,517 46,730 10.99 10,243 1,509 45,011 20.70 4,174 826 50,642	8.99	8,529 1,391 45,142 18.02
	NES en 6,050 3,226 41,769 18.17 11,340 2,377 45,181 23.29 6,040 4,111 48,091 17.43 10,115 4,855 40,875 26.81
	NES fr 6,021 3,996 49,185 16.92	8,699 2,632 49,949 18.49 7,356 4,524 53,868 18.07	7,394 4,172 43,602 20.97
	NES nl 5,585 2,308 44,605 15.03 10,416 1,625 44,722 21.21 4,708 1,084 49,850 10.41	8,770 1,627 44,665 18.88
			(b) The RSDO5 dataset.
	Languages	Biomechanics (bim) B I O % Term B	Chemistry (kem) I O % Term B	Veterinary (vet) I O % Term	B	Linguistics (ling) I O % Term
	sl	7,070 6,835 47,439 22.67 7,614 4,486 52,912 18.61 10,953 6,261 57,968 22.90 12,348 6,079 90,623 16.89

(a) The ACTER dataset.

Table 4 :

 4 Evaluation on ACTER given Heart failure as test set. .67 52.93 62.39 51.33 56.32 65.25 44.17 52.68 68.67 52.36 59.42 69.29 60.17 64.41 74.35 61.71 67.44 fr, nl 60.84 46.84 52.93 62.27 50.37 55.69 69.20 48.29 56.88 70.72 49.54 58.26 75.72 56.70 64.84 76.74 59.58 67.08 en, fr, nl 56.83 53.03 54.86 60.76 52.53 56.35 68.01 50.67 58.07 48.30 65.57 55.63 69.92 64.32 67.00 73.66 62.91 67.86 en, fr, nl, sl 45.88 66.29 54.23 48.30 65.57 55.63 58.10 61.62 59.81 59.48 62.51 60.96 62.74 75.51 68.54 63.57 73.69 68.26

	Train		Enlish test set				French test set				Dutch test set
	language		ANN			NES			ANN		NES			ANN			NES
		P	R	F1	P	R	F1	P	R	F1	P	R	F1	P	R	F1	P	R	F1
	en	58.08 48.12 52.63 62.07 52.03 56.61 66.69 47.89 55.75 70.63 53.79 61.07 69.23 61.09 64.91 72.95 63.04 67.63
	fr	56.94 33.21 41.95 60.01 39.07 47.33 70.51 44.43 54.51 72.41 48.53 58.11 72.12 51.01 59.76 73.63 55.50 63.29
	nl	55.64 56.37 56.00 57.60 58.34 57.97 66.49 51.48 58.03 67.60 53.16 59.52 70.25 62.15 65.95 73.29 61.49 66.87
	en, fr	57.16 51.21 54.02 60.43 51.45 55.58 63.70 52.38 57.49 68.13 52.78 59.48 72.52 61.72 66.69 73.08 63.49 67.95
	en, nl	58.00 48

Table 6 :

 6 The evaluation of monolingual and multilingual learning in RSDO5.

			Monolingual setup		multilingual setup
	Validation Testing					ANN			NES
			P	R	F1	P	R	F1	P	R	F1
	vet	ling	69.55 64.05 66.69 67.68 69.55 68.60 67.19 69.88 68.51
	bim	ling	69.48 73.66 71.51 69.78 66.16 67.92 67.81 68.53 68.17
	kem	ling	66.20 72.38 69.15 66.50 71.35 68.84 67.89 69.03 68.46
	ling	vet	71.06 66.72 68.82 70.96 65.27 68.00 69.22 67.40 68.30
	kem	vet	72.66 65.59 68.94 69.75 68.83 69.29 70.49 67.75 69.09
	bim	vet	69.30 68.07 68.68 69.77 68.43 69.09 69.26 64.72 66.91
	ling	kem	68.67 55.13 61.16 68.26 59.28 63.45 67.54 54.59 60.38
	bim	kem	70.14 60.27 64.83 69.63 61.19 65.14 69.25 52.72 59.86
	vet	kem	70.23 59.24 64.27 69.90 58.41 63.64 67.92 59.24 63.28
	vet	bim	63.51 66.80 65.11 61.14 64.94 62.98 60.94 66.67 63.68
	ling	bim	62.25 65.20 63.69 60.53 63.82 62.13 62.62 62.27 62.44
	kem	bim	62.35 63.99 63.16 65.71 59.16 62.26 61.78 67.05 64.31

Table 7 :

 7 Comparison between our performance and SOTA in RSDO5 dataset.

	Methods	Linguistics P R	F1	Veterinary P R	F1	P	Chemistry R	F1	Biomechanics P R F1
	Monolingual 69.48 73.66 71.51 72.66 65.59 68.94 70.14 60.27 64.83 63.51 66.80 65.11
	Multilingual 66.50 71.35 68.84 69.75 68.83 69.29 69.63 61.19 65.14 61.78 67.05 64.31
	SOTA [24] 52.20 25.40 34.10 66.90 19.30 29.90 47.80 31.40 37.80 53.80 24.80 33.90

Table 8 :

 8 Performance per term length per domain in each test set. ,858 70.62 77.29 2,062 2,103 1,448 70.22 68.85 1,073 1,202 768 71.58 63.89 1,153 1,172 822 71.29 70.14 3 322 360 7,191 59.32 53.06 314 446 182 57.96 40.81 164 260 93 56.71 35.77 223 286 124 55.61 43.36

										(a) ACTER test set.
			k	Enlish Preds GTs TPs P	French R Preds GTs TPs P	Dutch R Preds GTs TPs	P	R
			1 1,009 1,170 639 63.33 54.62 1,153 1,309 829 71.90 63.33 2,005 1,687 1,292 64.44 76.59
			2 985 801 501 50.86 62.55 490 620 320 65.31 51.61 661 391 303 45.84 77.49
			3 553 377 256 46.29 67.90 163 266 100 61.35 37.59 108 108 55 50.93 50.93
			4 163 142 86 52.76 60.56 47	91	24 51.06 26.37 19	35	10 52.63 28.57
			≥5 53	95	26 49.06 27.37 13	88	4 30.77 4.55	1	33	1 100.00 3.03
								(b) RSDO5 Linguistics test set.
	k	Linguistics Preds GTs TPs P	Veterinary R Preds GTs TPs P		Chemistry R Preds GTs TPs P	Biomechanics R Preds GTs TPs P	R
	1 2,078 1,728 1,300 62.56 75.23 2,159 2,067 1,472 68.18 71.21 943 890 580 61.51 65.17 1,079 718 22 48.38 72.70
	2 2,631 2,404 14 57 80	31 54.39 38.75 28	77	10 35.71 12.99 26	46	11 42.31 23.91 26	59	11 42.31 18.64
	≥5 12	29		79 75.00 31.03	3	55	2 66.67 3.64	3	11	0 0.00 0.00	11	84	5 45.45 5.95

https://www.clarin.si/repository/xmlui/handle/11356/1470

The detailed description of ACTER can be found in the TermEval competition[START_REF] Rigouts Terryn | TermEval 2020: Shared Task on Automatic Term Extraction Using the Annotated Corpora for Term Extraction Research (ACTER) Dataset[END_REF].

https://www.cjvt.si/rsdo/en/project/

Acknowledgements

The work was partially supported by the Slovenian Research Agency (ARRS) core research programme Knowledge Technologies (P2-0103), as well as the Ministry of Culture of Republic of Slovenia through project Development of Slovene in Digital Environment (RSDO). The first author was partly funded by Region Nouvelle Acquitaine. This work has also been supported by the TERMITRAD (2020-2019-8510010) project funded by the Nouvelle-Aquitaine Region, France.