Silvano Cincotti

Wolfram Elsner

Nathalie Lazaric

Anastasia Nesvetailova

Engelbert Stockhammer

Towards an Evolutionary Political Economy

Introduction

This is the first of two inaugural issues of the Review of Evolutionary Political Economy (REPE), a new peer-reviewed, inter-disciplinary academic journal, initiated by the European Association for Evolutionary Political Economy (EAEPE) and published by one of the leading international academic publishers, Springer Nature.

But why a new journal? Is there a potential space for it in the universe of already well-established economic journals? And what could its value-added for the economics community be?

The ambition of the journal is to bring together and integrate political economy, evolutionary and institutional economics, agent-based computational economics, and heterodox macroeconomics, and to become a pluralistic forum of excellence in what is emerging as evolutionary political economy.

Economics got separated from the social sciences in the late 19 th century, when its later mainstream turned from "political economy" to "pure economics", which in turn, led to "neoclassical economics." A substantial further narrowing of that mainstream took place from the 1970s onwards. With the "New Classical" counterrevolution, the Keynesian-neoclassical truce of the post-war period was ended. This transformed, in particular, the field of macroeconomics. A new mainstream economics was established, based on methodological individualism, particularly "rational expectations", and the notion of market clearing. "Proper" economics then had to use a particular mathematical modelling and the models themselves had to be based on "rational" agents. In "New Classical" macroeconomics, a rational-expectations and global-knowledge equilibrium became the reference point of modelling in the form of the dynamic stochastic general equilibrium (DSGE) models.

In economics, such reductionism marginalized the different strands of non-mainstream economics. It became more difficult for non-orthodox and epistemologically pluralistic economists to publish in orthodox journals, to win grant funding and get promotion.

But it also led to some fragmentation among the non-mainstream scene, where heterodox economists of Marxist, post-Keynesian, institutionalist, socio-economic, feminist, ecological, or neo-Schumpeterian economics developed their own fields, established journals and associations. And several generalist, heterodox and pluralist organizations and networks, EAEPE being prominent among them, emerged in order to facilitate discussion, interaction and potential convergence among the disparate heterodox streams.

REPE now has been founded on the conviction that the fragmentation of the heterodox economic perspectives and the separation of "economics" and "political economy" is a severe impediment to progress in economic research and relevant insight for our societies. Below, we will briefly give an outline of some research orientations in the different fields to be covered by REPE, before we will discuss what we see as the particular role and value-added of this journal.

Building blocks: Evolutionary Economics, Original Institutional Economics, Complexity Economics, post-Keynesian Economics, and International Political Economy

The ambition of Evolutionary Economics in general is to interpret change and to observe the development (or history) of socio-economic systems, either by a formal dynamic system or by its qualitative reconstruction (or by both). Evolutionary approaches, paradigms or theories integrate bounded rationality in dealing with complex problems, radical uncertainty, search, knowledge acquisition and learning (Dosi and Winter 2003: 286). They receive inspiration from biology and biological analogies, but also from other natural sciences with their complex dynamic approaches and network modelling, such as physics or neuroscience. Those insights have entailed a renewal in the economics discipline and a new broad interdisciplinary cooperation.

Most heterodoxies in economics, among them neo-Schumpeterian, Veblenian, radical, post-Keynesian, or Hayekian, interpret evolutionary dynamics in the context of socially learned and interactively acquired social rules and institutions, as coordination, cooperation, orientation and complexity-reducing devices, and as interim social "solutions", or transient "attractors" in complex dynamics, but also possibly as dysfunctional, "petrified" structures preventing favourable change, impeding innovation, or causing petrifactions of systems (e.g., [START_REF] Dopfer | The evolutionary foundations of economics[END_REF][START_REF] Witt | What is specific about evolutionary economics[END_REF]Elsner 2017a;Dosi et al. 2019). Such approaches rest upon a relatively recent transdisciplinary scientific corpus, characterised by a diversity of methods in addressing change, innovation, process, slower or faster transitions, and institutional and organisational solutions with shorter or longer, useful or hindering states of systems. In these approaches, there can be no unique and globally optimal way of doing things, but agents and organisations have to adhere to social institutions, habits, routines, or norms, which leads to systemic path dependence (e.g., [START_REF] Becker | Applying organizational routines in understanding organizational change[END_REF]). Evolutionary approaches have not been completely standardised yet as to their methods, giving rise to a profusion of answers to address the questions of dynamics, process, history, novelty, or disruption. But among key findings of evolutionary economics are explanations showing why the "optimal" technology may not be realized (e.g., [START_REF] David | Clio and the Economics of Qwerty[END_REF], why learning, adaptation, and building absorptive capacities are critical for technology diffusion, and how, once a technology is adopted, path dependency occurs, reinforcing prior choices (e.g., [START_REF] Cowan | Nuclear Power Reactors: A Study in Technological Lock-in[END_REF]. This was formally depicted first in W.B. Arthur's famous model of competing technologies [START_REF] Arthur | Competing Technologies, Increasing Returns, and Lock-In by Historical Events[END_REF].

Related applied evolutionary insights gained considerable impact on contemporary research and innovation policies, namely in the EU. Industrial and organisational economics, the evolution of demand and consumption, questions of the natural environment, or the natural commons, including the global climate, and the transformations of the capitalist system, all were substantially broadened by evolutionary approaches.

While neo-Schumpeterian evolutionary theory relies upon a positive vision of innovation, a vector of growth and progress taken as a whole (e.g., [START_REF] Dosi | Energy, Development, and the Environment: An Appraisal Three Decades After the "Limits to Growth" Debate[END_REF]Dosi et al. 2019), Veblenian evolutionary-institutional perspectives also focus on "ceremonial" degenerations, obstruction, distortion, or negative externalities of technologically innovative potentials (e.g., Elsner 2020).

The evolution of demand and consumption, analysed already by Veblen, was further developed more recently, for instance based on work by U. Witt andothers (e.g., Witt 2003, 2008;[START_REF] Lazaric | Determinants of sustainable consumption in France: The importance of social influence and environmental values[END_REF]). An evolutionary economics of consumption and demand scrutinizes preferences and values, which is symptomatic of evolutionary research responding to contemporary problems (e.g., Buenstorf and Cordes 2008;[START_REF] Cordes | Technological diffusion and preference learning in the world of Homo sustinens: The challenges for politics[END_REF]. This includes research within interdisciplinary so-called naturalistic perspectives, for instance on learning and transmission processes of new behaviour, such as in consumption qualified as "green". Work along these lines also makes evident the pertinence of environmental questions, which may become the new battle horse of evolutionary-institutional approaches as much as innovation and consumption, all in both research and policy (e.g., [START_REF] Maréchal | Overcoming inertia: insights from evolutionary economics into improved energy and climate policy[END_REF][START_REF] Brouillat | Dynamic efficiency of extended producer responsibility instruments in a simulation model of industrial dynamics[END_REF].

With respect to innovation, the long-standing lack of a critical stance towards innovation, failures to link innovation to social inclusiveness and ecological sustainability, and the reluctance to think about innovation in terms of governance and ownership have motivated the ambitions of the present journal. And there is much out already, where we can build upon (e.g., Metcalfe et al. 2005, Gifford and[START_REF] Gifford | Knowledge-Intensive Entrepreneurship and S3: Conceptualizing Strategies for Sustainability[END_REF][START_REF] Savona | Structural changes and sustainability. A selected review of the empirical evidence[END_REF].

While the evolutionary approach in a neo-Schumpeterian perspective focuses on innovation under the dialectic of destruction and creation and its different performances under more competitive versus oligopolistic and corporate conditions, the evolutionary-institutional approach in the Veblenian perspective (i.e., original institutional economics) focuses on the potentially different characters of social institutions as either problem-solving ("instrumental") or differential status and power preserving ("ceremonial") devices. And while neo-Schumpeterian economics has become leading in industrial economics, its ecologies, dynamics and evolution, and in research and innovation policies, the Veblenian neo-institutionalist "institutional dichotomy" has facilitated an operational and value-aware theory of "progressive" versus "regressive" institutional change to explain typical ongoing processes in contemporary capitalist socio-economies. Both evolutionary perspectives have considerably been further developed since the 1960s and 1970s into fruitful applied, applicable, empirically and policy-wise accessible research programmes. So, while neo-Schumpeterian economics has become the main paradigm shaping modern research and innovation policies, institutionalism, formerly looked at as "just historical" or "just sociological" by mainstream economists, had not only shaped US legal systems and social policies in the 1920s and 1930s, but also, with a strong epistemological value-awareness, developed a policy paradigm of a "negotiated", socially problem-solving economy, based on its elaborated "instrumental value principles".

And both variants of evolutionary economics today also are deeply involved in "hetecon" discourses with political economy, post-Keynesianism, international political economy, or complexity economics, where potential convergences and complementarities are being fathomed (e.g., [START_REF] Elsner | State and Future of the "Citadel" and of the Heterodoxies in Economics: Challenges and Dangers, Convergences and Cooperation[END_REF]Elsner , 2017b)). Their policy conceptions do benefit from cooperation with political economy, the policy orientations of post-Keynesian economics and the field of International Political Economy, as well as their comprehensions of systems are benefiting from modern complexity sciences.

Besides the research in each of these fields it is this structured interaction processes, discovering differences, complementarities, and convergences, that will take place in REPE and guide its discourses and its pluralistic heterodox editorial policy.

The science of complexity studies phenomena, which emerge from a collection of interacting entities and which cannot be understood by considering them as separate units. In this sense, the holistic approach does not exclude a reductionist theory, but states that the aggregate can possess emerging properties that are different and complex with respect to the mere superposition of elementary objects. Complexity was proposed in the 1970s as counter-paradigm to the reductionist methods in natural sciences (e.g., [START_REF] Anderson | More Is Different[END_REF], but already in 1959 and 1962, H.A. Simon prompted out the necessity of a holistic approach to the study of the economy, and in 1969, T.C. Schelling took up Simon's lesson with a study on segregation that led to first real applications of complexity sciences to social systems, with surprising, counterintuitive results. Since Simon's and Schelling's seminal works, the complexity approach has allowed to explain aggregate and emerging statistical regularities as well as both intuitive and counter-intuitive relationships in the economy, outcomes of the interaction of heterogeneous and bounded-rational agents in either multiple equilibria or disequilibrium conditions of imperfect decentralized systems (markets) (e.g., [START_REF] Kirman | Whom or What Does the Representative Individual Represent[END_REF].

Complexity economics offered the opportunity to address questions that cannot be tackled by means of representative and perfectly rational agents in general-equilibrium conditions, whereas realistic explanations involve notions of heterogeneity, adaptive expectations, social collective intelligence and self-fulfilling or self-destroying properties, interactions, contagion, networks, etc.

Complexity Economics is the answer to the needs of understanding all that, and it is nowadays the scientific area embracing all methodologies and applications of complexity sciences to economics, ranging from agent-based approaches (e.g., [START_REF] Arthur | Asset Pricing Under Endogenous Expectations in an Artificial Stock Market[END_REF][START_REF] Raberto | Agent-based simulation of a financial market[END_REF][START_REF] Delli Gatti | A new approach to business fluctuations: heterogeneous interacting agents, scaling laws and financial fragility[END_REF][START_REF] Baron | Agent-based Computational Finance[END_REF][START_REF] Buchanan | Meltdown modelling. Could agent-based computer models prevent another financial crisis[END_REF][START_REF] Cincotti | Credit money and macroeconomic instability in the agentbased model and simulator Eurace[END_REF][START_REF] Farmer | A complex systems approach to constructing better models for managing financial markets and the economy[END_REF]Dosi and Roventini 2019) to networks [START_REF] Battiston | Credit chains and bankruptcy propagation in production networks[END_REF][START_REF] Delli Gatti | The financial accelerator in an evolving credit network[END_REF][START_REF] Battiston | Complexity theory and financial regulation[END_REF], all in the general framework of non-linear dynamical complex systems (e.g., [START_REF] Arrow | Beyond General Equilibrium[END_REF][START_REF] Arthur | Complexity and the Economy[END_REF][START_REF] Beinhocker | The Origin of Wealth: Evolution, Complexity, and the Radical Remaking of Economics[END_REF][START_REF] Helbing | Rethinking Economics Using Complexity Theory[END_REF].

This also offers the opportunity to represent, analyse and study economic and financial systems as complex adaptive systems and to translate them into artificial economies gathering insights and outcomes from different heterodox economic approaches (e.g., innovation, endogenous money, or financial acceleration) and taking fruitful inspiration and positive feedbacks from other disciplines (e.g., mathematics, physics, engineering, biology, or computer science) in a creative and valuable inter-and multidisciplinary context.

Post-Keynesian economics is an important heterodox macroeconomic theory that emphasizes demand-led growth, involuntary unemployment and issues of financial instability (e.g., [START_REF] Lavoie | Introduction to Post-Keynesian Economics[END_REF][START_REF] Palley | Post Keynesian Economics. Debt, Distribution and the Macro Economy[END_REF][START_REF] King | A History of Post-Keynesian Economics since 1936[END_REF][START_REF] Hein | Post-Keynesian macroeconomics since the mid-1990s -main developments[END_REF]. It builds on the radical insights of J.M. Keynes, in particular the concept of fundamental uncertainty, which implies that people cannot act in globally rational form and will resort to behavioural rules, which are often stabilized by social institutions and norms. This has important implications for the theory of money demand (liquidity preference), financial stability (the possibility of herding) and for investment dynamics, where non-rational elements, Keynes' "animal spirits", play an important role.

Post-Keynesian economics' initial project was to develop Keynes' macroeconomic theory, which had a short-term focus, into a growth theory [START_REF] Robinson | The accumulation of capital[END_REF][START_REF] Kaldor | Alternative Theories of Distribution[END_REF][START_REF] Kaldor | A model of economic growth[END_REF][START_REF] Kalecki | Theory of Economic Dynamics[END_REF]) and to critique neoclassical theory of production and distribution (Cambridge capital controversies, e.g., [START_REF] Harcourt | Some Cambridge Controversies in the Theory of Capital[END_REF][START_REF] Moss | The end of orthodox capital theory[END_REF]. It is based on a class-analytic approach and conventional, real-world behaviour rather than methodological individualism. Also post-Keynesian economics formed into a distinct school of thought with its own journals and networks in the 1970s. Since then it has developed theories of endogenous money creation (e.g., Moore, Lavoie), financial instability, and endogenous financial cycles (e.g., [START_REF] Minsky | Stabilizing an unstable economy[END_REF][START_REF] Palley | Debt, aggregate demand, and the business cycle: an analysis in the spirit of Kaldor and Minsky[END_REF][START_REF] Ryoo | Household debt and housing bubble: a Minskyan approach to boom-bust cycles[END_REF]; Nikolaidi and Stockhammer 2018 as a survey), demand regimes and growth models (e.g., [START_REF] Bhaduri | Unemployment and the real wage: the economic basis for contesting political ideologies[END_REF][START_REF] Lavoie | Wage-led growth: Concept, theories and policies[END_REF], stock-flow consistent modelling (Godley and Lavoie 2006). There also is a closely related stream of Latin American structuralism, which analyses the growth conditions and constraints in developing countries (e.g., [START_REF] Cimoli | Technology, structural change and BOP-constrained growth: a structuralist toolbox[END_REF].

Post-Keynesians have commented extensively economic policy making (e.g., [START_REF] Hein | The crisis of finance-dominated capitalism in the euro area, deficiencies in the economic policy architecture, and deflationary stagnation policies[END_REF][START_REF] Stockhammer | Wage-led growth: theory, evidence, policy[END_REF][START_REF] Palley | Escaping stagnation and restoring shared prosperity: a macroeconomic policy framework for job-rich growth[END_REF], in particular questions of fiscal and monetary policy, critiqued austerity policies and highlighted the danger of rising debt ratios for financial stability. Post-Keynesian economics has established itself as an alternative to mainstream macroeconomic theory addressing questions of demand formation, economic growth, distribution, and financial instability.

Compared to other heterodox approaches, post-Keynesian economics has paid less attention to modelling individual behaviour, but there are several areas of ongoing cooperation and intellectual cross fertilization with other heterodox approaches. As of yet, these are often limited to specific niches. For example, there is a recent wave of papers to integrate non-mainstream macroeconomics more systematically into ecological economics, and climate-change models in particular (e.g., [START_REF] Dafermos | A stock-flow-fund ecological macroeconomic model[END_REF]). There also is an exciting intersection of agent-based modelling and post-Keynesian economics (e.g., Caiani et al. 2016;Dosi et al. 2010); and there is a growing interest in the field of comparative political economy in the post-Keynesian analysis of demand regimes and growth models (e.g., Baccaro and Pontussen 2016;Schwartz and Tranoy 2019).

It is for such boundary-crossing research that REPE will be a forum.

Although it has long historical roots in moral philosophy and political economy, the contemporary discipline of International Political Economy (IPE) would re-emerge only in the late 1960s and 1970s at the intersection of international economics and international relations. At the time of the global ascent of market-centred ideologies and policies some economists, as well as other social scientists, became dissatisfied with the lack of attention paid to of power relations in traditional economics. The analysis of power, in its multiple facets, had been central to political science and the study of international relations. The latter, in turn, traditionally focused on the analysis of monetary factors and trade regimes in shaping international economic diplomacy. While the founders of IPE included economists as well as political scientists, today the discipline is typically hosted in international relations and political science departments.

At present, IPE is divided by the so-called Atlantic divide [START_REF] Cohen | The transatlantic divide: Why are American and British IPE so different[END_REF]. The American tradition of IPE has evolved by adopting the methods of neoclassical economics to the study of international trade diplomacy. As a result, American IPE today is dominated by quantitative methodologies and is defined by the focus on one main puzzle: Namely, if free markets are optimal allocators of scarce resources, how come that governments around the world continue to adhere to protectionism? Addressing this puzzle, American IPE employs, for instance, the method of two-level game theory, modelling the response of international economic agents against the context of (1) international and (2) domestic constituencies.

On the other side of the Atlantic divide, IPE has much stronger roots in classical political economy and history of economic thought, especially its Marxist tradition. This particular stream of scholarship remains prominent in the UK and continental Europe, where historically IPE has drawn on the French School of Regulation. The main themes for IPE in Europe have been globalisation, neoliberalism, and the changing world order.

There is a third school of thought, somewhat less widely known, that informs IPE scholarship today. It goes back to the tradition of Original Institutional Economics (OIE), the above-mentioned evolutionary-institutional economics (or institutionalism), and the works of T.B. Veblen, J.R. Commons, A.A. Berle and others. Its key focus has been twofold: On the one hand, it is the understanding of the structure and nature of wealth and capital; on the other, it is the relationship between individual and aggregate forms of organization and behaviour in capitalism. Although somewhat interrupted in its development by the ascent of quantitative approaches in mid-20 th century, such evolutionary IPE has been given a boost by the 2007-09 crisis. From around that time, it has been developing through a close dialogue between institutional economics, political economy, economic sociology, and law.

In this process, the focus on uncertainty and expectations, historically central to post-Keynesian thought, has been advanced also by insights from OIE. Today, the essence of this approach is best captured by what Perry Mehrling called the "finance view" of the economy, which focuses on the present valuations of capital assets, seeing them as entirely dependent on imagined future cash flows, projected back into the present (Mehrling 2011: 4).

It is against this background that the discipline of IPE can particularly benefit from this new journal, REPE, focusing on inter-disciplinary research in evolutionary political economy. It seems to become increasingly clear that today's economic institutions and structures are defined by expectations of change and valuations of the future. It also appears to be increasingly clear that big politicaleconomic issues of today and tomorrow are likely to centre not on isolated or individual problems and crises, but on complex, systemic phenomena and processes. REPE is intended as an outlet that can inform the emergent evolutionary strand in IPE and related disciplines. The articles that comprise the inaugural issues should initiate discussions about the ways such discourses may develop.

Evolutionary economics, institutionalist economics, complexity theory, post-Keynesian economics and International Political Economy all have important developed alternative perspectives to current mainstream economics. They all share a rejection of the rational-agent methodological individualism and of simple market clearing. Instead, they advocate an understanding of human behaviour as shaped by social institutions and conventions, of decentralized ("market") processes as prone to evolutionary dynamics that may lead to socially undesirable results and give rise to fallacies of composition.

Thus, we think that the various non-mainstream approaches in economics and the social sciences already share some common ontological, theoretical, and policy-oriented ground, but they have developed in different fields and have built their own niches.

We think it is important to build more bridges between these and also other heterodox approaches (like feminist or ecological economics) to support the works towards comprehensive alternative visions of economics that could at some point absorb and replace the orthodox economic mainstream in research, applications, advice, policy-making, teaching and textbook writing.

Background and agenda of REPE

While initiated by EAEPE, REPE is organisationally independent as an academic journal and will be strictly international globally, both in its published articles and its personnel and governance (Editorial team, Associate Editors, and International Advisory Board). EAEPE severely discussed the question of founding a new journal for several years. It considered that there are already a number of excellent heterodox and pluralist journals existing. There are wellestablished journals for evolutionary economics, institutional economics, political economy, post-Keynesian macroeconomics, complexity economics, and International Political Economy, furthermore, for innovation or history of economic thought, in Schumpeterian, Marxian, socialeconomic, institutionalist, Austrian, feminist, ecological and other critical perspectives. There are also journals, both of a heterodox-pluralist and mainstream self-image, that cover cutting-edge methodologies, complex systems, networks, or computational approaches. But the editorial team of REPE, EAEPE, and Springer did identify a space indeed for a new pluralist-heterodox journal, which relates cutting-edge evolutionary and complex systems with institutional analysis, critical political economy, heterodox macroeconomics, computational methods, and interdisciplinarity, in a number of applied fields, from innovation, networks, distribution, or social class to climate change, with topicality and critical policy relevance, wherever possible.

For this, as our Aims&Scope and Call for Papers clarify, we invite high-quality and cutting-edge conceptual and review articles, theoretical, methodological, empirical, and computational research from the fields and cross-fields mentioned. Submitted papers should embrace the heterogeneity of economic agents and interactions as complexified by power, institutions, or environmental conditions. Papers might explore the origins and impact of economic crises, inequality and unemployment, the changing role of finance and financial instability, the digital transformation, artificial intelligence, the socio-economic facets of environmental degradation and climate change, up to most topical issues of economic impacts of epidemics. Articles that advance novel interdisciplinary analyses of complex systems, embrace social, natural, behavioural, and computer sciences or draw on humanities and cultural theories are highly welcome.

As a key purpose of REPE is to bring together different heterodox contributions, as sketched above, we will also rely on symposia (with 3-4 papers) and special issues (with 6-7 papers) on topical themes or themes that cut across conventional themes and disciplines and relate in some way to what may emerge as evolutionary political economy. Ideas and proposals from potential guest-editors are invited. Brief instructions are to be found on REPE's website: https://www.springer.com/journal/43253. Each author will assign his/her paper to one of the major fields given by the Editorial Manager system when submitting. This should not be a restriction to the author but an assistance for quickly assigning the handling editor and reviewers.

Finally, to be successful, this journal needs support and critical feedback from critical scholars, including those beyond EAEPE. The editorial team always welcomes comments on REPE's agenda and issues.

Outline of the present first inaugural issue (issue 1-2020)

Against the background outlined, for the inaugural issues, we invited some of the leading figures in evolutionary political economy to reflect the status and future of what is, what could and will be a research programme of evolutionary political economy. It turned out that we will be able to present to the scholarly public two inaugural issues, the present one and the next one, issue 2-2020, which will appear in Fall.

Smita Srinivas' article "Development, Institutional Variety, and the Future of Economics" explains the importance for development economics of diverse ontological assumptions about the nature and content of development for understanding and tackling institutional variety. The differences notably matter for understanding technological and social capabilities. Using four cases drawn from the author's research, this article highlights diverse conceptual frameworks for institutional variety. Such variety matters for economic development, and it is critical to understand local conditions and history of such variety. Indeed, industrialization is a process of institutional change, where social capabilities provide direction and guidelines for development goals and institutional transformation. By scrutinizing the process of inference during the building of capabilities, evolutionary political economy may improve the understanding of the emergence of that institutional variety and its outcomes within and across countries.

In "The past, present and future of evolutionary macroeconomics", Malcolm Sawyer argues that heterodox macroeconomics contains many features of evolutionary political economy. It highlights social conflict and potential instabilities of monetary production economies in a world of fundamental uncertainty. Heterodox macroeconomics thus features reoccurring crises, distributional conflict and gives rise to a path-dependent growth process. Sawyer particularly discusses three frontiers of heterodox macroeconomics that in the past have received limited attention: ecological issues and climate change, gender, and heterogenous-agent modelling. Overall, he concludes that heterodox macroeconomics is part of evolutionary political economy that analyses monetary production economies.

In "Gender and the Future of Macroeconomics: an evolutionary approach", Sheila Dow argues that gender lends itself well to an evolutionary analysis which focuses on non-equilibrium change and transformation for individuals within society. Decomposition by this important category, gender, helps us to understand the economy at the macro level, and design macroeconomic policy better. It also provides the foundation for advocating equal gender rights and outcomes. In contrast, in mainstream macroeconomics, gender issues can only be partially analysed due to its requirement of "microfoundations", its focus on GDP growth, and its explanation of suboptimal outcomes as due to market imperfections only. An approach which takes gender seriously requires the different epistemology that arises from feminism: It does not rely on dualistic categorisations, but builds on the idea of situated knowledge, which in turn requires a pluralist methodology and an acceptance of fundamental uncertainty. Such a methodology allows for emergent identity, for the cognitive roles of emotion and social convention, and for attention to power relations beyond market power.

John Davis' paper "Belief reversals as phase transitions and economic fragility: A complexity theory of financial cycles with reflexive agents" contributes to the analysis of expectations and belief reversals in an evolutionary and complexity economics framework. It formulates its analysis in terms of the concept of reflexivity, drawing on ideas on reflexivity in financial markets of G. Soros. It lays out a model of how a financial cycle expresses a systematic pattern of interacting feedback effects. The paper develops this analysis as a complex interaction between sets of heterogeneous expectations derived from the behavior of reflexive economic agents. A central role is played by agents' beliefs and judgments underlying their expectations, and how those beliefs and judgments in uncertain circumstances are changeable and subject to abrupt reversals, which can manifest themselves in 'Minsky moments.' Agents' belief reversals may follow their misconceptions about causal processes in booms and upswings, and such misconceptions will reflect their tendency to think causally in terms of negative feedback patterns rather than positive ones.

In "Was Hyman Minsky a post-Keynesian economist?", Marc Lavoie discusses key features, and strengths and weaknesses, of Minsky's financial instability hypothesis, which became popular in the aftermath of the failure of Lehman Brothers despite having been ignored by mainstream economists until then. Minsky is an interesting case as he has been important within post-Keynesian theory, but was a student of Schumpeter's. His analysis of financial innovation and endogenous financial crisis has institutionalist and evolutionary features, while the core of his macroeconomics, including endogenous money creation via credit, and the principle of effective demand have clearly Keynesian features. While Colander, Holt andRosser (2010) andDavidson (2003-4) have questioned whether Minsky can be classified, Lavoie concludes that indeed Minsky clearly stood in the post-Keynesian camp.

Frank Beckenbach's article, "A value-theoretic approach to economic dynamics and evolution -Synthesizing different Marxian modules in a simulation model. Part I: Foundations", has a twofold background: first, the ongoing debate about whether the Marxian theory of value has been damaged (or even destroyed) by the alleged impossibility of solving the "transformation problem" and, secondly, the fact that almost all of the (later) economic manuscripts of Marx are now accessible due to the MEGA project. In regard to the former, it can be concluded that the rationale of the Marxian value concept is distorted by its Ricardian elements (including the labour theory of value). In regard to the latter, the later economic manuscripts as well as the few economic monographs published during Marx's lifetime indicate that Marx was figuring out different modules for his value theory which are not thoroughly synthesized with each other. An attempt is made to reformulate this contested and fragmented value theoretic torso by focussing on the non-Ricardian essentials of the value theory. This (de-)construction is then used as a basis for linking the different value-theoretic modules in terms of their ability to explain economic evolution. In addition to a conceptual

discussion of the value-theoretic modules, a simulation model is suggested for their integration. It can be shown that the value theory can be substantiated in a consistent stepwise fashion and the conditions for generating the long-term results expected by Marx can be specified.

As REPE will be a topical journal as well, with a variety of formats of scholarly production published, we decided to add a current paper on the economic dimension of the Corona-pandemic under our irregular rubric "Brief Communication". It is the paper "Behavioral economics in the time of Coronavirus: Rebellion or 'willful ignorance' in the face of 'grand Challenges'" by Pritika Rao.

As said, issue 2 will provide the second part of our two inaugural issues and will appear after the summer break with more papers on the future of the fields of evolutionary political economy by prominent authors (P. Arestis, F. Beckenbach, L. Chester, K. Dopfer, M. Lavoie, R. Palan, J. Toporowski, U. Witt). Enjoy reading REPE!


Laurine Huber

Chaker Memmadi

Mathilde Dargnat
email: mathilde.dargnat@univ-lorraine.fr

Yannick Toussaint
email: yannick.toussaint@loria.frchaker.memmadi@outlook.fr

Do Sentence Embeddings Capture Discourse Properties of Sentences from Scientific Abstracts ?

We introduce four tasks designed to determine which sentence encoders best capture discourse properties of sentences from scientific abstracts, namely coherence between clauses of a sentence, and discourse relations within sentences. We show that even if contextual encoders such as BERT or SciBERT encodes the coherence in discourse units, they do not help to predict three discourse relations commonly used in scientific abstracts. We discuss what these results underline, namely that these discourse relations are based on particular phrasing that allow non-contextual encoders to perform well.

Introduction

This paper compares the ability of different sentence encoders at representing coherence of sentences from scientific abstracts, and more specifically discourse relations between clauses.

Our first hypothesis is that BERT [START_REF] Devlin | BERT: Pre-training of deep bidirectional transformers for language understanding[END_REF] and SciBERT [START_REF] Beltagy | SciB-ERT: A pretrained language model for scientific text[END_REF] models enable to produce sentence representations that take coherence into account thanks to their training done on Next Sentence Prediction. Our second hypothesis is that this training should also enable to capture discourse relations between clauses.

Discourse relations (DRs) represent the semantic and pragmatic links between discourse units (DUs), that are either clauses, sentences or groups thereof, within a hierarchical structure that represents the whole text. In this work, we focus on sentences that are defined as textual sequences separated by a period. Sentences may comprise one or more DUs, and when they have at least two DUs, the coherence links between them is represented through DRs. These DRs are either explicitly signaled, or left implicit. For example, the sentence "By wearing a mask, we can protect the others." conveys an enablement relation between the action "wearing a mask" and the event "protecting the others", which is here lexicalized by the connective "by". DRs can be used to extract new knowledge, especially in scientific abstracts which are highly structured [START_REF] Duross | The discourse-level structure of empirical abstracts: An exploratory study[END_REF].

Sentence embeddings (SE) represent the meaning of a sentence in a fixed-size vector space, and recent contextual approaches such as BERT have shown promising results for downstream tasks such as Semantic Textual Similarity (STS) or Natural Language Inference (NLI) [START_REF] Reimers | Sentence-BERT: Sentence embeddings using Siamese BERTnetworks[END_REF]. When further trained on downstream tasks, these models have shown promising results. However, their performance also rely on linguistic knowledge acquired at pre-training. For example, BERT and SciBERT are trained on both Masked Language Model and Next Sentence Prediction tasks, in order to capture general linguistic properties that are then transferred to learn more specific representations.

In this work, we want to understand if discourse properties are embedded in sentence representations that are built before further training on downstream tasks.

We design probing tasks, that are classification tasks whose goals are to predict discourse properties of the sentences from their embedding. Our goal is to highlight if discourse properties of sentences are captured by those vectors without finetuning. We rely on the corpus SciDTB [START_REF] Yang | SciDTB: Discourse dependency TreeBank for scientific abstracts[END_REF] to build four datasets used to probe if embeddings capture some discourse properties of the sentences. The two first datasets probe the coherence of sentences, and the two others probe the presence of DRs. We use four different sentence encoders to produce sentence embeddings, that we then use as input vectors for two classification models. If the classifier succeeds, it means that the vectors stores the discourse property that is probed. We evaluate the classifiers, thus high-lighting encoders that best encode the properties we probe.

This paper is organized as follows. We first provide background to our work. Second, we detail the tasks that we design to detect discourse properties. Third, we present our experimental setup, including the different SE that we evaluate and the choices that are specific to the corpus on which we rely. Finally, we present our results and discuss some issues.

Background

Coherence and cohesion are two key notions in the perception of a text as a unified whole.

Coherence refers to logical and semantic relations between clauses and sentences in a text, while cohesion refers to grammatical or lexical devices such as pronouns, verb tense or connectives, that form external relations of a text [START_REF] Halliday | Cohesion in english[END_REF]. While the former may stand without the latter and vice versa, the connectivity model of [START_REF] Renkema | The Texture of Discourse[END_REF] mixes coherence and cohesion cues. In this work we follow this approach, and will refer to as coherence the properties of both coherence and cohesion.

DRs may either explicitly signal coherence relations by discourse connectives, or left them implicit. In this latter case, the reader infers the relations based on coherence links between clauses. For example, the marker "to" is frequently used to links an action X and a way Y to realize X, signaling that Y is the manner-means to do X. However, even if some relations are explicitly signaled with discourse connectives such as "to" or "by", others are less salient. For example in "We propose a novel extension of this work using target context information.", the ellipsis of "by" make it harder to infer the relation. Some of them (e.g enablement, manner-means or attribution) express a logical link between the content of the clauses they relate, while others (e.g elaboration or progression) only express a continuation or additive relation. In scientific abstracts especially, DRs that convey a logical link are often lexically marked.

Several theories such as Rhetorical Structure Theory (RST) [START_REF] Taboada | Rhetorical structure theory: Looking back and moving ahead[END_REF] or Segmented Discourse Representation Theory (SDRT) [START_REF] Lascarides | Segmented Discourse Representation Theory: Dynamic Semantics With Discourse Structure[END_REF] help to build text discourse structures, by providing both sets of DRs that are defined based on the content of the DUs and a framework for attaching the DUs by means of DRs. The RST defines a set of relations1 that are either mononuclear (if for two DUs, one is more salient than the other) or multinuclear (if two or more DUs have the same importance). They serve to build the hierarchical discourse structure of the text, either as a constituency tree, or, more recently, with dependency trees [START_REF] Morey | A dependency perspective on RST discourse parsing and evaluation[END_REF], which are used to annotate scientific abstracts from SciDTB [START_REF] Yang | SciDTB: Discourse dependency TreeBank for scientific abstracts[END_REF].

SE have shown promising results for a wide variety of NLP tasks [START_REF] Conneau | SentEval: An evaluation toolkit for universal sentence representations[END_REF], but are not interpretable independently of the others, making it unclear what linguistic information they contain and what is the meaning that they represent exactly. Previous works [START_REF] Shi | Does string-based neural MT learn source syntax?[END_REF][START_REF] Adi | Fine-grained analysis of sentence embeddings using auxiliary prediction tasks[END_REF]Conneau et al., 2018) intended to clarify what linguistic information are contained in SE by designing auxiliary tasks that take a single sentence vector as input and tries to predict a simple linguistic property of the sentence. They showed that some syntactic properties (e.g word order or syntactic tree depth) and some properties that they defined as semantic2 (e.g the tense of the main verb or the number of the subject) are well captured by SE. We then hypothesize that the latter may also capture discourse coherence and DRs in sentences from scientific abstracts, thus being the first step to extracting information at a semantic-pragmatic level.

Probing tasks

In this section, for each probing task, we explain how we build a dataset of sentences tagged with discourse properties. They are then encoded by various methods and used to train and evaluate classifiers. We introduce two tasks designed to check if coherence between clauses of a sentence is encoded (3.1, 3.2), and two tasks to check if DRs in sentences are encoded (3.3, 3.4).

Swapped units detection

Ordering of clauses fully participates to sentences coherence. We evaluate if coherence is captured by SE by evaluating the ability of a classifier to distinguish between coherent sentences and incoherent ones. We produce incoherent sentences by swap-ping two adjacent discourse units from sentences originally correct.

The dataset for this probing task thus consists of original multi-clausal sentences and of their incoherent equivalent obtained by swapping two random adjacent DUs. By doing so, we however do not ensure that new sentences are always incoherent. For example, swapping units 0 and 1 in the sentence "[Word alignment] [using recencyvector based approach] [has recently become popular.]" produces the new coherent sentence "[Using recency-vector based approach] [word alignment] [has recently become popular.]".

The resulting task is a binary classification task into {yes, no}, where yes corresponds to swapped sentences, and no to the original sentences.

Scrambled sentence detection

Topic incoherence may emerge when a topic T 1 is involved in the context of another topic T 2 , leading to incoherence because of the incompatibility between T 1 and T 2 .

The dataset used to probe this property is built as follows. Starting from original multi-clausal sentences splitted in DUs, we replace a randomly chosen DU by another randomly chosen DU from a different document, thus changing the context of a DU. Here again, the new sentence might be still coherent. For example, replacing the second DU in the sentence "[But these methods cannot be used][to obtain the estimates of causal effects-the quantity of interest for applied researchers.]" produces a new coherent sentence "[But these methods cannot be used] [using a two-phase approach.]".

The resulting task is a binary classification task into {yes, no}, where yes corresponds to scrambled sentences, and no to the original sentences.

Relation detection

A sentence may either be composed of a single DU, or of multiple DUs. In the last case, the DUs may be links through additive or continuation DRs (in bold in Table 1), or through logical relations (in italics in Table 1). We distinguish them and evaluate if SE can be used to predict whether a sentence contains a logical DR.

We rely on the discourse annotations provided in SciDTB, from which we extract the subtrees corresponding to the discourse structures of the sentences. Sentences that contain only one DU and sentences made of multiple DUs whose subtree contains no logical DRs are classified as norel. Others are classified as rel regardless of the type of the logical DR they contain.

The resulting task is a binary classification task into {rel, norel}.

Relation semantics detection

Being able to precisely identify which logical discourse relation is involved in a sentence may be useful as a first step toward knowledge extraction from texts. This task thus evaluates if the semantics of the DRs involved in sentences are represented in their embedding.

We rely again on the annotations to classify sentences based on the DR they contain. Sentences that contain only one DU and sentences made of multiple DU whose subtree contains no logical DRs are classified as norel. Others are classified based on the relation r they contain, if and only if they contain only one of these relations. The sentences that contain two or more logical relations are not tackled in our approach, and are thus not considered in the dataset.

The resulting task is a K-classes classification task where K is the number of relations that are considered.

Experiments

Data

SciDTB3 is a corpus of 798 scientific abstracts from ACL Anthology4 . Abstracts are segmented into DUs in a semi-automatic way, following the guidelines of [START_REF] Carlson | Building a Discourse-Tagged Corpus in the Framework of Rhetorical Structure Theory[END_REF], and annotated by discourse structure in dependency with DRs from the RST relations set, which was slightly modified and extended to be adapted to scientific abstracts5 .

The majority of documents contain between 5 and 7 sentences (minimum 2, maximum 14, mean 6), resulting in a set of 4196 sentences in total. Among them, 787 are made of a single DU, thus containing no relation, and 3409 contain more than one DU, and thus at least one DR. We use the segmentation into DUs to produce the datasets for coherence detection tasks (swapped and scrambled). We use the inter-clausal DRs to produce the datasets for DR detection tasks (binRel and semRel) 6 .

For swapped and scrambled, each sentence that has more than one DU is classified as no, and is used to produce a new sentence that is classified as yes, resulting in a dataset of 6818 sentences. For each sentence (original and modified), we ensure that punctuation and case do not bias the experiment by removing capitalization, periods and commas.

For binRel and semRel, we need to form classes that are broad enough to train the classifiers. It leads us to make choices because of the limited size of the corpus. Fig. 2 shows the number of sentences having one of the given DRs. Most of the DRs are involved in less than 200 sentences, which is not enough for training a classifier. Among the others, three relations are additive relations: Same-Unit links two segments of a DU broken into two parts, Joint, links two DUs which are in conjunction, and for Elaboration-addition one DU gives additional information to another DU 7 . Moreover, the latter are involved in most of sentences made of more than 3 DUs, and are thus often used together with logical DRs. We decide to ignore them, which allows us to build a sufficiently 6 We make the resulting datasets available at https://gitlab.com/laurinehu/scidtb_ expe/-/tree/master/probing_datasets. 7 We admit however that saying that Elaboration-addition does not contain an interesting semantics is somehow arbitrary, but can be understood by comparison with the semantics of other DRs. large dataset of sentences, tagged with the three relations: enablement, manner-means or attribution. We assume that the classifier will therefore learn to predict one of these relations.

Sentence embeddings

In this section, we present the different SE that we study, most of them being obtained by using pretrained models available on HuggingFace's [START_REF] Wolf | Huggingface's transformers: Stateof-the-art natural language processing[END_REF]) website 8 . We compare SE obtained by averaging non-contextual word vectors (bag-ofvectors), and SE obtained by using more recent language contextual models based on transformers that have shown promising results for various tasks. Comparing the last to bag-of-vectors enables us to determine whether coherence or DRs are only captured because of linguistic cues, or because of implicit links between clauses.

Global word Vectors [START_REF] Pennington | GloVe: Global vectors for word representation[END_REF]) are word representations obtained from aggregated global word-word co-occurrence statistics from a large crawled corpus. Training objective is to learn word vectors such that their dot product is the log of the word's probability of co-occurrences. We use the largest pre-trained model available, constituted of a vocabulary of 2.2M words, and containing vectors of dimension 300. We build sentence vectors by computing the mean of the word vectors, thus producing bag-of-vectors (BoV) of dimension 300.

Google USE [START_REF] Cer | Universal sentence encoder[END_REF] uses a Deep Averaging Network that is first trained in an unsupervised way as in Skip-Thoughts [START_REF] Kiros | Skip-thought vectors[END_REF], and whose output is transferred to be further trained on a supervised way on Natural Language Inference task. This encoder was the state-of-theart before more recent contextual approaches with which we compare it. We use the largest model, which encodes sentences into vectors of dimension 512.

BERT [START_REF] Devlin | BERT: Pre-training of deep bidirectional transformers for language understanding[END_REF] is the current stateof-the-art for most NLP tasks. It produces contextual word representations, from training a bidirectional encoder on two tasks: the first called Masked LM (MLM) predicts a masked word from its left and right context, and the second called Next Sentence Prediction (NSP) predicts whether from two sentences A and B, B is the actual sentence that follows A. BERT thus represents the meaning of a word according to its immediate context, but also on the basis of relationships between sentences. We use BERT-base model, from which we recover the last hidden state after having processed the sentence in the model, thus producing vectors of dimension 768.

SciBERT [START_REF] Beltagy | SciB-ERT: A pretrained language model for scientific text[END_REF]) is a BERT model specifically trained on full papers from the corpus of Semantic Scholar, thus seeking to provide a better representation of scientific vocabulary. We use SciBERT-base and proceed in the same way as for BERT, obtaining vectors of the same dimension.

For GloVe, BERT and SciBERT, we calculate the embedding e(S) of the sentence S by calculating the mean of each word vector as defined in 1.

e(S) = 1

|S| w∈S e(w)

(1)

Classification

Because we deal with datasets of different size and vectors made of latent variables, we train and evaluate two different classifiers for each task, namely a Logistic Regression (LR) and a Multi-Layer-Perceptron (MLP) with one hidden layer and three hidden units. On the one hand, the datasets are small in size, which can make the training of the MLP hard and a good LR performance possible.

On the other hand, the properties that we probe may not be linearly separable, which would make them hard to tackle with a LR classifier. The comparison of two classifiers thus gives us a fine-grained analysis of both the results obtained and the way in which the properties are encoded.

Experimental set-up We use the evaluation toolkit Senteval 9 [START_REF] Conneau | SentEval: An evaluation toolkit for universal sentence representations[END_REF], and keep the parameters as defined in it, namely the optimizer is RmsProp [START_REF] Tijmen | Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude[END_REF], the batch size is 164 and the loss function is crossentropy. Because the number of sentences that we have is quite small, we proceed with 5-fold cross validation. We compute the mean of accuracies at each fold. To compute the test accuracy, we keep the best model and test it on the testing set (10%) of the data. We compare both results to ensure that the model is not overfitting.

Results

In this section, we comment the results obtained for each probing tasks. Table 3 shows the test accuracy and the mean accuracy of 5-fold cross validation. We only comment the latter, as it is more relevant due to the small datasets we have.

The LR classifier obtains better results than the MLP for all tasks. Although losses in accuracy when using MLP can be explained by the lack of data, LR gives good results with BERT and SciB-ERT for binRel, swapped and scrambled, suggesting that those properties may be encoded linearly in the vectors.

Relation detection DR are almost as well encoded by BoV than by BERT and SciBERT, which can be explained by the fact that they are most of the time lexicalized. Contextual embeddings slightly improve the results for binRel (from ≈ 73% for BoV to 76.4 ± 1.5), but not for semRel (61.3% for BoV to 57.9 ± 2.2).

By comparing two by two the results of BoV, BERT, and SciBERT encoders for binRel, we highlight that those models nearly give the same predictions (see Table. 4). Among the 290 sentences used as test, 94 are predicted differently by BERT and BoV, 85 by SciBERT and BoV, and 67 by SciBERT and BERT. Among the sentences that are similarly predicted, less than 20% correspond to common mistakes, and the others to common good predictions. For BERT and SciBERT, around 70% of the predictions are similar, showing that the models capture similar information for this task. For BERT and SciBERT with BoV respectively, more than 50% are common, but still they make a lot of different predictions, showing that they do not capture same aspects of the sentence.

For semRel however, the models make very different predictions. Among the 144 examples used as test, 47 are predicted differently by BERT and BoV, 78 by SciBERT and BoV, and 71 by SciBERT and BERT. The results of Table 5 are consonant with this variation, showing that all encoders have in fact different abilities for the prediction.

We show both precision and recall obtained for SemRel in Table 5. For attribution detection, BoV, BERT-uncased and SciBERT-cased gives the best recall (81%), for enablement it is BERT-uncased (67%), for manner-means it is BoV (78%), and for norel it is SciBERT-uncased (75%). These good recalls obviously result in lower precisions. However, as we want to find as many instances of a relation R, rather than maximizing the number of instances that are correctly found, we are still satisfied.

Coherence detection Representations build from BoV fail at predicting if units have been swapped (51%10 ), and perform badly for predicting if units have been scrambled (≈ 51.9%). Google Universal Sentence Encoder improves swapped only by 10% and scrambled by 4%. Representations built from contextual embeddings improve accuracy for swapped by almost 26% for BERT and 27.5% for SciBERT and for scrambled by almost 25% for BERT and 27.8% for SciBERT. This shows that training on STS, on which Google USE is trained, is not enough to capture coherence links between clauses, and that the "Next Sentence Prediction" (NSP) task on which both BERT and SciBERT are trained seems indeed to help a lot. This task enables somehow to account for the order of the units in the sentence. Even if BERT and SciBERT are not trained at clause level, the corpus on which they are trained is large and therefore contains a large number of sentences that are made of a single clause. This enables the model to learn clause level contingency relations, which are in fact the task that we probe here.

Discussion

In this section, we discuss potential biases in the probing tasks that we designed, how we control them, and the possible improvements that could be further done.

Relation prediction A bias that could affect DR detection is the length of the sentence. In particular, Conneau et al. (2018) showed that BoV obtain 66.6% accuracy for predicting the length of the sentence, and get up to 99% with other more elaborated encoders such as BiLSTM or gated convolutional networks. We took that into account when selecting the different sentence sets. By including sentences that have more than one DU (sentences that only have relations with weak semantics) in the norel class , we somehow control the size of the sentences and ensure that the distributions of words per sentence are not specific to each class. The distributions are given in Fig. 3 , and are close enough to guarantee that the Sentence Length does not bias our predictions for binRel. Similarly, we ensured that the length of the sentence does not affect the detection of the relation semantics. The distributions which are given in Fig. 4 are close and, so, cannot be the only parameter involved in the good quality of the prediction.

However, good performances of BoV for this task are influenced by the fact that most of them are linguistically signalled. Even if this bias is in fact related to the specific genre of the corpus that we study, we discuss here the linguistic properties that play a role in the various encodings and predictions.

Verbs such as "we show" or "we demonstrate" typically occur in sentences which contain an attribution relation. Conjunctions such as "to" or "in order to" express enablement relations. Finally, manner-means are often signalled by verb forms such as "(by) +ing". We think that these signals play an important role in the good performances of BoV especially for attribution and manner-means.

Manner-means

The sentence "Most sentence embedding models typically represent each sentence only using word surface, which makes these models indiscriminate for ubiquitous homonymy and polysemy" is a typical example of the sentences whose relation is predicted well with BoV encoders but neither by BERT, which predicts an attribution, nor by SciBERT, which predicts noRel. The verb "using" seem to be responsible for this good prediction. This underlies the good recall that BoV gets for the prediction of this DR, which gets a very bad recall for BERT and SciB-ERT.

Attribution The sentence "[Most studies on statistical Korean word spacing do not utilize the information] 1 [provided by the input sentence] 2 [and assume] 3 [that it was completely concate- nated ] 4." is well predicted by all encoders, except SciBERT. Here, the captured relationship is a manner-means between units 3 and 4, which is in fact the deepest DR in the full structure of the sentence, and thus the less important. The second part (3+4) of the sentence is in conjunction with the first (1+2) and refers to it. The manner-means between 3 and 4 is thus not the more salient in the RST, but is however well predicted. This suggests that the choices that we did to ignore non-logical DRs work well and that the training made on sufficient data suffice to make the classifier focus on the relations that we consider.

Enablement With BERT, most of the sentences that contain the connective "to" are well detected as an enablement relation. Among those that are not well classified, most of them are classified as attribution or norel. One common characteristic to the sentences that are classified as norel, is that they are often long and contain more than 2 DUs and several discourse connectives, including "to" or "for". It is thus harder for the model to understand and distinguish the role of the connectives, as they are drowned in a lot of lexical information, leading to errors because of the difficulty of the task. For sentences that are wrongly classified as attribution, we observe a similar behaviour. The sentence "[Recent work has shown success] [in using continuous word embeddings] [to improve supervised NLP systems.]" contains an enablement between segments 1 and 2, but is classified as an attribution. This errors seems to be due to the verb "shown" that appears a lot in sentences that contain an attribution. This shows that even if BERT improves a sort of coherence detection, it in fact relies a lot on lexical cues for detecting the semantics of relations.

Swapped and scrambled

We admit that the tasks designed for probing coherence are somehow arbitrary, and produces different biases that could be better controlled. We present two major issues, and propose solutions for future work.

The first issue concerns the swapped task, where modifications of sentences may produce other coherent sentences, which are thus tagged as incoherent in the training set and used as it by the classifier. Those cases specifically correspond to sentences that are formed by two DUs and linked by a particle (such as "by", "for" or "to"), which are thus swapped and still coherent. For example, the sentence "[By incorporating textual information,][RCM can effectively deal with data sparseness problem.]" becomes "[RCM can effectively deal with data sparseness problem][by incorporating textual information.]". We expect such sentences to be poorly predicted, but it is not the case, showing that BERT and SciBERT probably rely on other indices. Conneau et al. (2018) also created acceptable sentences for three of their probing tasks, and filtered them by crowd-sourcing, asking people to rate sentences according to their acceptability. This method would be the best suited to solve this issue, as it may be hard to control coherence automatically with syntactic parsers.

Secondly, we did not ensure that the property captured does not rely on syntax, because sentence modifications may also produce sentences that are syntactically incorrect and that we did not filter out. Here the problematic cases mostly come from two clauses sentences containing a conjunction such as "that" or "and". For example the sentence "[We present a human judgments dataset] [and an adapted metric for evaluation of Arabic machine translation.]" becomes "[And an adapted metric for evaluation of Arabic machine translation] [we present a human judgments dataset.]", thus being obviously syntactically incorrect, and possibly captured as incoherent based on this syntactic property. A possible solution to that issue could be to forbid the swap of the first and last DU, thus inducing another different bias and in our case reducing the size of the corpus. Another possibility is to introduce rules based on the syntactic tree of new sentences in order to filter those cases. In this preliminary work, we introduced the second coherence detection task (scrambled) with the aim of reducing those biases.

Conclusion

We introduced four tasks for probing the discourse properties involved in sentences from scientific abstracts. We evaluated the ability of a classifier to predict a discourse property of a sentence from its embedding. The performance of the classifier highlights the extent to which discourse properties are encoded in those representations.

We showed that coherence links are captured by vectors made from contextual models, as well as DRs, but that those models in fact do not encode the semantics of those DRs. We highlighted that BoV embeddings perform nearly as good as contextual embeddings for both DRs detection tasks, highlighting the fact that these relations are most of the time explicitly signalled in scientific abstracts.

This confirms our hypothesis that BERT and SciBERT training suffice to encode coherence to some extent. We think that the Next Sentence Prediction task is the reason for this performance, as it allows the model to learn adjacency properties of sentences, and thus of clauses. The second hypothesis however is not confirmed, as we concluded from the experiments that semantics of DRs are as well captured by BoV, as BERT or SciBERT.

The present work opens various possibilities, for improving the tasks as well as the analysis of the results. We decided here to focus on the detection of three specific relations that occur within sentences. We adopted strategies to make this possible, for example by ignoring DRs that are of continuation or additive and not logical. We could however go further by considering all relations involved in the sentences. A multi-label classification could be a solution to the problem of predicting all DRs of a sentence from its embedding. A Sequence-to-Sequence model could be a solution to the problem of predicting the discourse structure (or sequence of relations) from its embedding. For coherence prediction, we also plan to determine to what extent the tasks we introduced (swapped and scrambled) depend on syntactic properties. A statistical analysis of the syntactic structures involved could help to clarify the possible biases coming from the syntax. We also plan to further check the datasets that we introduce for those tasks, as we have raised that our method created coherent sentences, labeled as incoherent in our training dataset.

Other corpora such as RST-DT [START_REF] Daniel Marcu Carlson | Rst discourse treebank[END_REF] or PDTB [START_REF] Webber | A short introduction to the penn discourse treebank[END_REF], which could be combined, could be used to produce other datasets for probing DRs, enabling to evaluate how other DRs (such as contrast or reason) are encoded in sentence embeddings.

Figure 1 :

 1 Figure 1: Example of dependency annotation from SciDTB.

Figure 2 :

 2 Figure 2: Distribution of relations in sentences.

Figure 3 :

 3 Figure 3: Distribution of the number of words per sentences for binRel.

Figure 4 :

 4 Figure 4: Distribution of the number words per sentences for semRel.

Table 1 :

 1 Discourse relations in SciDTB corpus

	Attribution Background	Cause-effect
	Comparison	Condition	Contrast
	Elaboration Enablement	Evaluation
	Explain	Joint	Manner-means
	Progression Same-unit	Summary
		Temporal

Table 2 :

 2 Table 2 summarizes the datasets used for each probing task, where the classes for each task are balanced. Datasets description.

		classes	# sent # sent / class
	binRel	rel	2894	1447
		norel
	semRel	attribution	1432	358
		enablement
		manner -means
		norel
	swapped	yes	6818	3409
		no
	scrambled	yes	6818	3409
		no

Table 4 :

 4 Two-by-two comparison of the predictions obtained by different encoders for binRel and semRel.

			binRel			semRel
		BERT-BoV SciBERT-BoV SciBERT-BoV BERT-BoV SciBERT-BoV SciBERT-BoV
	# common predictions	196	205	223	97	66	73
	# different predictions	94	85	67	47	78	71
	# common errors	162	175	180	64	54	73

Table 5 :

 5 Precision and Recall for SemRel.

			BoV Google USE BERT uncased BERT cased SciBERT uncased SciBERT cased
	Precision Attribution	66	62	78	85	96	74
		Enablement	70	44	48	46	100	71
		Manner-Means	65	36	73	55	43	58
		NoRel	70	69	55	70	38	53
	Recall	Attribution	81	78	81	78	61	81
		Enablement	58	19	67	58	14	47
		Manner-Means	78	69	53	64	53	58
		NoRel	53	25	47	44	75	67

https://www.sfu.ca/rst/01intro/ definitions.html

They admit however that the boundary they propose between syntactic and semantic tasks is somewhat arbitrary.

https://github.com/PKU-TANGENT/SciDTB

https://www.aclweb.org/anthology/

We refer the reader to the paper[START_REF] Yang | SciDTB: Discourse dependency TreeBank for scientific abstracts[END_REF] to get explanations on the adaptations and annotation procedure. The relations set is recalled in Table1.

https://huggingface.co/

We consider that an accuracy close or less than 50% corresponds to random prediction, because a classifier predicting each class with a probability of 0.5 would have an accuracy of 50%.

Acknowledgments

We want to thank the reviewers for their constructive comments and suggestions.

This work was supported partly by the french PIA project "Lorraine Université d'Excellence", reference ANR-15-IDEX-04-LUE.