ILCEA4 Caroline Rossi

Jean-Pierre Chevrot

Uses and perceptions of Machine Translation at the European Commission

Keywords: Machine translation (MT), post-editing (PE) Technology Acceptance Models (TAMs), Directorate-General for translation (DGT), human-computer interaction, ergonomics, human factors

Current research on translation technology seeks to integrate physical, cognitive and organisational ergonomics, and uses insights from the situated cognition paradigm to bring together social and technical perspectives on fast-evolving human-computer interactions. Even though these trends imply that a wider variety of professional contexts should be considered, studies of institutional translation are still scarce. This paper reports on a three-week research stay in the French language department of the European Commission (DGT-Fr2), aimed at understanding current uses and perceptions of machine translation (MT) and post-editing within Europe's biggest translation institution. Based on ethnographic data, we established a survey that we tested among French translators before translating it into English and submitting it to all DGT translators. Our quantitative data include 89 respondents from 15 language departments. We perform multiple linear regressions to assess technology acceptance, before focusing on the variance that the model leaves unexplained. Our findings show that perceptions of control, subjective norm and image, as well as insecurity (fear of MT) have an impact on professional MT acceptance.

Introduction

Current research on translation technology seeks to integrate physical, cognitive and organisational ergonomics (Lavault-Olléon 2011) and uses insights from the situated cognition paradigm [START_REF] Martín | On paradigms and cognitive translatology[END_REF][START_REF] Risku | A Cognitive scientific view on technical communication and translation: Do embodiment and situatedness really make a difference?[END_REF] to bring together social and technical perspectives on fast-evolving humancomputer interactions [START_REF] O'brien | Translation as human-computer interaction[END_REF][START_REF] Cadwell | Human factors in machine translation and post-editing among institutional translators[END_REF]. Even though these trends imply that a wider variety of professional contexts should be considered, studies of "translating institutions" are still scarce [START_REF] Koskinen | Translating institutions: An ethnographic study of EU translation[END_REF].

This paper analyses the data gathered during and following a three-week research stay in the French language department of the European Commission's Directorate-General for translation (DGT-Fr2). Our overarching aim is to understand current uses and perceptions of machine translation (MT) and post-editing (PE) within Europe's biggest translation service. We used existing scales and models designed to study technology acceptance and combined them with ethnographic data that helped us adjust to the specific institutional setting under study. In what follows, we start by describing the context of MT at the DGT (Section 2), before detailing the theoretical framework and methodological triangulation that we used to design a large-scale survey (Section 3). The results are presented using both descriptive statistics and linear regressions in Section 4, and discussed in Section 5.

MT at the European Commission's DGT

The DGT is one of a kind: not only is it "[a]rguably the largest translation agency in the world" (Koskinen 2008: 69), but its mission is also unparalleled, notably in terms of "the unprecedented scale of their multilingual operations and the legal and political importance of translation" (Svoboda et al. 2017: 2). The production of 24 different language versions of each piece of EU legislation, as per Council Regulation No 1 of 1958, is indeed key in making sure all EU citizens get the information they need about their rights. The DGT is also unparalleled in terms of its human, linguistic and financial resources, which make it a model for translator trainers (Rossi 2017: 50). These elements are central to the following statement of the DGT's vision:

We aspire to be a full partner in the legislative and communication processes, the hub for all translation-related activities at the Commission and a reference in the world of translation, while contributing to the development of each official language and the translation profession (DG-Translation, 2016b: 3).

However, the DGT currently works under tight budgetary constraints: in accordance with the objective of "reducing the cost of translation", the DGT implements "continued staff reductions" while "meeting unflagging demand" (DG-Translation 2017: 3). Thus, while the number of translated pages is now well over two million, the number of translators has gone down from about 1,750 in 2015 (Cadwell et al. 2016: 226) to about 1,560 as we write this paper (DGT, personal communication). Action has been taken to bridge the resulting gap, which is also patent in overall DGT staff, as shown in Figure 1. Official documents indicate that this action has been of two kinds. First, outsourcing has increased steadily. The 2016 activity report indicates that the proportion of pages translated externally (as a percentage of the total number of translated pages delivered) has grown from 27% in 2015 to 29.5% in 2016, and is set to reach about 31% in 2018 (DG-Translation 2016a: 6). Second, automation and the continuous improvement of language services and tools -notably the Commission's MT engine -have made an important contribution. In the next section, we review major changes leading to the current workflow.

Translation tools and workflow: automation at the DGT

The European Commission's MT system was developed with a view to building a Digital Single Market: ever since its creation, the chief objective of MT@EC 1 has been to "help European and national public administrations exchange information across language barriers in the EU, Iceland and Norway" (European Commission 2016: 5). The Commission's system has nonetheless been "offered by the European Commission to translators in the EU institutions' translation departments" since its initial development in 2010, and MT@EC is now deeply integrated in the current workflow. It has actually been cast as an additional resource, though one that ought to be taken with a "pinch of salt" (Cadwell et al. 2016: 228), i.e. used critically by translators, who were thus encouraged to deepen their awareness and understanding of MT.

As shown in Figure 2 below, automation starts when a translation request is sent (through the "Poetry" portal) to the DGT's central translation memory (TM) called Euramis: this allows all relevant previously translated content to be automatically retrieved and stored in a local translation project. Managers (heads of units) get projects in ManDesk and translators start from those projects to create the translation file in TraDesk. All projects integrate MT@EC outputs together with TMs, but with a strong penalty automatically assigned to the former. As they select resources, translators can untick the MT output and thus choose not to use it at all. The auto-suggest function in their current translation environment (based on SDL Trados Studio) also enables them to discard the MT output and continue typing. Finally, the circular flow in Figure 2 shows that all final versions of translated documents are sent back to Euramis, which will be used in its updated version for each new round of MT training.

Finally, automation is also applied to outsourcing: part of the streamlined workflow described above has been used since October 2016, enabling freelance translators to work in a similar translation environment and (most importantly) with the same resources as DGT translators: "outsourcing with sdlxliff files was introduced as the standard outsourcing method" (DG-Translation 2017: 16). We hope to have shown that technology is currently ubiquitous at the DGT, and that translation tasks are embedded in an extremely well-designed workflow. Within this intricate interweaving of technology and human intervention, machine translation, which appears only in the form of easily dismissed segments, may well be of minor importance and its impact limited. To date, however, very few studies have assessed the impact of MT's inclusion in translation workflows, which "has led to a need for more in-depth studies on the ergonomics and human factors associated with MT use" (Cadwell et al. 2016: 225). In what follows, we explain how we went about analysing translators' interactions with MT in terms of both uses and perceptions.

Assessing uses and perceptions of MT in DGT translators

In order to take into account the specific institutional environment that we described in the previous section, we started by immersing ourselves in the daily routines of the French language department, which welcomed one of us as a visiting professor. Caroline Rossi did unstructured observation in and around DGT-FR.2 for three weeks in January 2017, and conducted semi-directed interviews to gather qualitative data that would help us ask relevant questions to a larger population of translators. This section describes the different steps that led to the design of a large-scale survey on current uses and perceptions of MT at the DGT. The questions we asked in the ethnographic phase of the study are provided in Appendix 1. The survey questions are provided in Appendix 2 together with descriptive statistics.

Ethnographic data

The ethnographic data collected during our stay comprises field notes of our daily observations, as well as 10 semi-directed interviews. We noticed that overall MT acceptance was quite high and daily use could be observed in most cases. It was also very clear that whilst many translators did choose to use MT, at least some of them also opted out of using the MT outputs in a number of instances. We noticed that there were individual patterns of use that were at least partly linked with the source language(s) the translator was working with. However, considering the sheer number of language combinations that we were likely to find when surveying the whole DGT (as of 2016, MT@EC covered "552 language pairs with 62 direct language pairs", Cadwell et al. 2016: 227) we decided not to discriminate between language pairs in our survey.

In observing procedures related to automation, we also considered the relatively recent appointment of three "workflow managers", who remain active translators among the 30+ members of DGT-FR2: their role is to ease and optimise the assignment of translation projects across the team. This enables everyone to work more smoothly, but some of the translators also noticed that it was much nicer to be able to discuss the translation project with the head of unit, as was the case previously when he/she brought the project in person to each individual translator. Although they apply to a specific situation, such remarks are a clear instance of what [START_REF] Marshman | In the driver's seat: Perceptions of control as indicators of language professionals' satisfaction with technologies in the workplace[END_REF]: 10, in LeBlanc 2017: 59) describes as an effect of "the human factors and policies in tools' implementation" rather than the technology itself.

Semi-directed interviews helped us gather more data on those human factors, and we were especially interested in how translators described their interactions with MT. Based on Cadwell et al.'s landmark study, we expected to gain rich insights into human factors associated with MT at the DGT. This is because DGT translators enjoy "relative job security, recognition, and a freedom not typically enjoyed by professional translators in other settings" (Cadwell et al. 2016: 329) which in turn allows for "the needs, abilities, limitations, and well-being of translators to be matters of central concern when considering MT adoption" (ibid.).

There was diversity in our data, confirming that translation technologies "are moulded by and impact upon humans in all sorts of ways" (Kenny 2017: 1). Among translators voicing neutral to positive attitudes to MT, MT@EC was in most cases cast as a typing aid and/or as a mere time saver, with no mention of annoyance and no affective reaction. On the other hand, more critical depictions of MT were associated with affective reactions. These included humorous personifications of MT as both an annoying salesperson repeatedly offering their silly solution (« la solution qu'elle essaye de te fourguer depuis le début » [the solution that the system has been trying to fob me off with from the beginning]) and a scapegoat that translators were quick to blame when they were dissatisfied and frustrated as a result of time pressure (« il faut bien qu'on passe nos nerfs sur quelqu'un » [we have to take it out on someone]).

One of our questions (see Appendix 1) helped us assess general knowledge about how MT works: all answers indicated good overall knowledge. Interestingly, however, the first reply that we got consistently amounted to confessing ignorance, and there were many expressions of uncertainty even in the most informed answers. Everything translators said about their knowledge helped us design a survey question (Appendix 2, question 25), to see whether similar knowledge would be found across the DGT, and if it could be linked with uses and perceptions in any way.

Finally, the answers we received to our question about future uses of TMs and MT in about ten years' time were surprisingly emphatic on the amount of change that translators expected. Statements were usually based on an appreciation of the rapid changes that translators had witnessed (including the "irresistible rise" of MT) but they also brought a number of expressions of fear that we had not expected: MT was cast as a threat to the human translator in four cases out of ten. On the other hand, translators with neutral to positive attitudes to MT were rather optimistic and mentioned the "constant progress" that was likely to help them more and more. In order to test for the existence of such polarisation across the DGT, we chose to reuse a survey question that we had recently tested and validated with trainee translators [START_REF] Rossi | Introducing statistical machine translation in translator training: from uses and perceptions to course design, and back again[END_REF]): answers on a 5-point Likert scale ranged from an appreciation of MT as very threatening, and up to very helpful (Appendix 2, Question 22).

Technology acceptance models (TAMs)

The core constructs used in our survey are those that are at the heart of TAMs, a series of models developed from a highly successful proposal: For each construct in the model, measures are achieved using a series of indicators. Having tested and validated reliability using multiple-question scales in a preliminary version of our survey, we avoided repetition of questions bearing on the same construct in the final survey, whenever we could, so as not to over-burden respondents.

Critical approaches

Existing critiques of TAMs stress that they are individual models that fail to capture social and organisational dimensions (see e.g. Legris et al. 2003: 202). As for organisational dimensions, we expected DGT translators to be a rather homogeneous social group, and one in which translators enjoyed "freedom, recognition and relative job security", as explained above based on Cadwell et al. (2016: 329). Indeed, one important feature was that translators genuinely had a choice, so the construct of (perceived) voluntariness was not relevant to our setting. Our ethnographic data clearly confirm Cadwell et al.'s results in this respect: not only were translators free to choose which TMs (including MT), and even which translation environment they worked with, but we did see them choose and adjust a number of times, expressing preferences that were based on their experience and expertise. No current version of TAMs is likely to capture such elements, which we believe are extremely specific to institutional translation, and maybe even to the DGT only. What we may expect as a result is more unexplained variation.

Social dimensions, on the other hand, are only included in TAMs in terms of the social influences constraining technology acceptance -namely, image and subjective norm. The models do not include other aspects, such as the social origins, dynamics and consequences of translation technologies for professional translators. One idea that might help in understanding those social aspects, and which our statistics are not likely to capture, is that technology is not neutral and its impact will depend on the context in which it is developed and promoted. This is what Science and Technology Studies, as well as social-constructivist approaches have shown (Kenny 2017: 2; Olohan 2017: 270). Interestingly, while MT has been cast as a necessity with respect to sustaining a multilingual Europe, especially with the recent development of the CEF platform 3 , no such determinism appears in discourse about MT@EC development for translators' use at the DGT, even though considerable effort and attention have been dedicated to it. What has been visible, however, and potentially perceived as a threat by some, was the huge investment in technology development, at a time when fewer translators were hired:

The European Commission has already invested more than €200 million over the last seven years on research and innovation in language technologies that have the potential to break through language barriers [START_REF] Ansip | How multilingual is Europe's digital single market?[END_REF].

Overall, our ethnographic data revealed good MT acceptance and frequent use, as well as patent variation and polarisation on the assessment of future prospects. In order to capture this variation, taking into account social and institutional aspects, we included questions about the translators' profile.

In the next section, we give an overview of survey categories and constructs before moving on to presenting our results.

Results

Survey categories and constructs

Our survey had 89 responses from 15 language departments. The first series of questions concerned experience (age and date of arrival at the Commission), academic background and/or training and language department. We hypothesised that, taken together or separately, these elements might point us to the existence of subgroups. Language departments were especially likely to represent distinct groups:

With little official guidance of translation policy, and with each unit comprised of just one nationality (or, in the case of languages spoken in more than one member state, a few nationalities), the structure supports the development of separate translation cultures within each unit. (Koskinen 2008: 70).

However, at least one question revealed surprising homogeneity, namely question 6 (see Appendix 2) in which we asked translators to choose from a series of 4 definitions of a translator and left one field blank for alternative definitions. The vast majority of translators (N=56) chose the definition given within the framework of the Interpretive Theory of Translation, and the rest overwhelmingly (N=31) opted for a definition that pictured the art of "go[ing] unnoticed". Questions we have not mentioned so far were added to the survey to provide more qualitative assessments: we could not discuss them all here owing to lack of space, but they can still be consulted in Appendix 2.

Participant profiles

Data collection was not easy and it appeared that translators were already asked to complete internal surveys regularly and had little time for it. Our quantitative data did not allow for the delineation of subgroups, and our sample was not balanced for gender, as it included 54 women and 34 men (one respondent did not want this information to be used). We do not see this as a problem: although we were hoping to have a more balanced sample, there are no pointers in the literature suggesting gender may have an impact on technology acceptance and use.

More importantly, our sample was well balanced with respect to age. Although we gathered answers from more than half of the 24 different language departments, we found strong disparities in the number of responding translators in each department.

We put together the answers to questions on actual use of MT (9 and 9') to derive a 0-5 score (i.e. Never: 0, A few times a year: 1, Monthly: 2, Weekly: 3, Daily: 4 and Systematically 5). Average scores were then plotted by language departments, and we checked these results against existing measures of MT adoption [START_REF] Foti | The Commission's machine translation service[END_REF][START_REF] Kluvanec | From machine translation at the European Commission to European language resource coordination (ELRC)[END_REF]) to see how representative our sample was. According to the latest annual evaluation (Kluvanec, 2017: slide 20) the departments with the lowest MT adoption rates are EN, DE, FI, LV and NL. Those with the highest are ES, FR, IT, MT, PT and RO. This distinction seems to apply to our data, with the remarkable exception of French, which based on our data is in the low-use group of departments. It is also worth mentioning that the English language department gets the lowest of all average scores: among 16 surveyed translators, only one used MT almost systematically. French and English, the only two language departments that depart from previously established adoption rates are among the three procedural languages at the Commission, i.e. the language departments that deal with the greatest diversity of text types and gather larger numbers of translators. Thus, we may hypothesise that in those departments, survey answers will vary according to diversity of respondents or subtle differences in questions used to assess actual use.

Linear regressions

TAMs were designed to predict IT use, and in order to achieve this, linear regressions have been the most used statistical models (Legris et al. 2003: 196). Overall, TAMs routinely account for 40% to 50% of variance in usage intentions, and only 30% of the variance in use (Venkatesh and Bala 2008: 291). As explained above, however, we did not assess usage intentions: this is because we expected, based on our ethnographic data and on the existing literature, that usage intentions would be more or less similar to actual use. In line with this expectation, we found strong similarity between actual use, as rated in answers to Question 10, and estimated sustainable use (which was assessed in Question 11): Cronbach's alpha is very high (0.912).

We followed our model (as described above in Figure 3) and performed three multiple linear regressions. We first sought to account for perceived usefulness based on the following six factors: experience, subjective norm, image, job relevance, MT output quality and perceived ease of use. The regression showed that, taken together, those factors accounted for 60.5% (adjusted R-squared 0.605) of all observed variation. However, only three factors made a significant contribution, namely perceived ease of use (standardised beta coefficient: 0.654, p<0.001), subjective norm (standardised beta coefficient: -0.150, p=0.053) and image (standardised beta coefficient: 0.200, p=0.025). This means that experience, job relevance and MT output quality contributed virtually nothing to the observed variations in perceived usefulness.

In a second regression, we tried to predict perceived ease of use with our measures of computer anxiety and control. This was less successful: we predicted only 35.1% of variation in perceived ease of use. Only control, as measured by Question 15, was significant (standardised beta coefficient: 0.580, p<0.001). A very low Cronbach's alpha (0.112) showed that our first measure of control, assessing translators' perception of autonomy (Question 8, based on Marshman 2012), was unrelated to that second measure. Besides, our results suggest that we may have failed to measure computer anxiety properly: one reason for this was that we chose to ask a question with reference to the current translation environment of DGT translators rather than computers in general.

In a final regression, we sought to account for actual use based on the following predictors: experience, perceived usefulness and perceived ease of use, fear (rated on a scale of security) and perceived impact of MT. With an adjusted R-squared of 0.517, the regression significantly predicted slightly more than half of the observed variation. However, two of the factors that are an essential component of TAMs failed to make significant predictions, namely experience (standardised beta coefficient: -0.009, p=0.900) and perceived usefulness (standardised beta coefficient: 0.087, p=0.464). Perceived ease of use did appear to have a mildly significant impact (standardised beta coefficient: 0.257, p=0.034). Above all, the two factors that we had introduced turned out to have more weight than traditional TAM measures: fear made a very significant prediction (standardised beta coefficient: 0.377, p<0.001), and perceived impact was also significant (standardised beta coefficient: 0.257, p=0.003).

Discussion

Seeking to understand why fear of MT should relate to variations in actual use as it does, we looked at translators' knowledge of MT (Figure 7) and found that there was a significant correlation between fear (i.e. a low degree of security) and knowledge of MT, as assessed by questions 22 and 25 respectively (Pearson correlation, r=-0.269, n=89, p=0.011). Thus, the translators who perceived MT as a threat were regularly those with the lowest scores for MT knowledge. Although there was good overall knowledge in our sample, with just over half of the translators getting the right answer, and another 27 having understood that EURAMIS was used, this was obviously not enough to "empower rather than marginalise translators" with MT (Kenny and Doherty 2014: 276). Such findings suggest that it would be worth investigating how much knowledge is needed to alleviate fears. Besides, our respondents were not at all convinced that special skills were needed to use MT (45 thought so but 44 did not). Their hesitation could stem from the perfect integration of MT within the streamlined workflow that we described in the second part of this paper, and the resulting lack of direct interaction of translators with MT@EC. This is coherent with Kenny and Doherty's (2014) holistic and empowering approach to teaching machine translation, as well as with a recent proposal that human-computer interactions (HCIs) could be enhanced by giving translators more control over their translation environments (Van den Bergh et al. 2015: 115). It would certainly take another thorough study to explore if and how this could be done at the DGT. Fostering translators' participation in the study as actors rather than mere respondents (see e.g. [START_REF] Ospina | Taking the action turn: Lessons from bringing participation to qualitative research[END_REF]) might be a way of achieving this aim, while ensuring better dissemination of the results among DGT translators. The existing DGT CATE Lab (Computer-Assisted Translation Environment Lab) is one such initiative, aimed at "involving DGT users in active technology watch" [START_REF] Travnickova | DGT Cate Lab[END_REF], and it would probably welcome new collaborations with academics.

The second element that our study reveals is the need to distinguish translation tasks from the translator's activity when analysing HCIs. The distinction is at the heart of both ergonomics and functional approaches to translation (Lavault-Olléon 2011), but TAMs are more focused on tasks, which may account for their failure to convey a clear picture of translators' technology acceptance. We have shown that tasks were integrated in a smooth and automatised workflow that eased management in a number of ways, but this is not likely to have much impact on the translator's activity.

Our first linear regression shows that constructs such as MT output quality are virtually unrelated to translators' perceived usefulness of MT, suggesting that relatively good MT output may still hinder the translator's activity in at least some circumstances. On the other hand, cognitive load, as well as different perceptions and conceptions of the systems are an integral part of the complexity that characterises such activity: they are captured by the three constructs that have significantly contributed to variations in perceived usefulness, namely perceived ease of use, subjective norm and image.

Finally, our data contain evidence for rich and creative interactions with MT in post-editing, as can be seen in Figure 8. The diagram is based on our recoding of individual definitions of a usable MT output. It shows that although 37% of translators mostly agree to interact with MT to save time, the remaining translators mostly use MT outputs for inspiration, terminology, or in a restricted set of contexts. We may also note that another 25% of our sample provided negative answers, based on the contexts in which they discarded MT (because of a lack of appropriate structure, or when the output did not make any sense). Such interpretive flexibility is evidence that there is room for creativity and expression of individual preferences at the DGT. The categories that we built, based on translators' open-ended responses to question 20, are necessarily restrictive: there is variation within those categories, but all answers are evidence that translators know very well when to use MT and why they do when they choose to. Thus, our categories should not be taken as evidence that guidelines could be produced to help translators or companies decide.

On the contrary, they show that translators' expertise can and should indeed be trusted when interacting with MT. Leaving it up to the translator to decide when it is appropriate to use MT and when not also opens up new perspectives for translator trainers. By asking students to choose and comment upon their choices, trainers would encourage them to think critically about MT in very concrete ways, and they could even lead them to question and better understand their own cognitive processes (Lavault-Olléon and Carré 2012).

Conclusion

In the present study, we sought to understand current uses and perceptions of machine translation (MT) and post-editing (PE) at the DGT, and we relied both on ethnographic methods and on survey analysis to account for technology acceptance. Although technology acceptance is high at the DGT, our results suggest that perceptions of MT have a strong impact on both perceived usefulness and actual use, and that HCIs actually designate a wide range of experiences and feelings that existing models partly fail to capture.

It is our hope that we, as translator trainers, will be able to characterise translators' roles in relation to MT in very diverse ways, thus opening up perspectives instead of shattering hopes.

6. Which is the best definition of a translator, according to you? -"A brilliant translator is one who goes unnoticed" (Chassigneux) (31) -"The best-case scenario is for translation to defend and illustrate the target language and culture" (Gouadec) (0) -"Translator traitor" (traduttore traditore) (0)

Figure 1 .

 1 Figure 1. Evolution in number of translated pages and DGT staff, including translators and other staff members (Source: DGT).

Figure

  Figure 2. The DGT's streamlined workflow (Source: European Commission, 2016: 5).

"

  [t]he most widely employed model of IT adoption and use is the technology acceptance model (TAM) that has been shown to be highly predictive of IT adoption and use "(Venkatesh and Bala 2008: 274). We chose to use them since they appeared to be the most robust, well-established instruments that could give us a coherent picture of the factors related to an individual's acceptance and use of a new technology. It has to be said, however, that TAMs have been explicitly built with a view to predicting "individual adoption and use of new ITs"(Venkatesh and Bala 2008: 275) thus helping managers to design successful interventions. While the focus of TAMs is more on the technology itself and the individual factors that are likely to foster acceptance and success, we started out with human factors and used TAMs as part of a larger evaluation of MT acceptance and use. This accounts for the parsimonious addition of factors in the model (namely "fear of MT" 2 and "perceived impact of MT"), as well as for the deletion of elements that were not relevant to current uses of MT at the DGT. Figure3illustrates our adapted version of the third TAM proposed by[START_REF] Venkatesh | Technology Acceptance Model 3 and a Research Agenda on Interventions[END_REF], i.e. the hypothesised interactions between the core constructs used to design the survey.

Figure 3 .

 3 Figure 3. An adapted version of TAM3 (Venkatesh and Bala 2008: 280).

Figure 4 .

 4 Figure 4. Number of respondents per age range.

Figure 5 .Figure 6 .

 56 Figure 5. Number of translators who took the survey broken down by language department.

Figure 7 .

 7 Figure 7. Translators' knowledge of MT.

Figure 8 .

 8 Figure 8. What defines a usable MT output?

Table 1

 1

	Question	Construct	Source
	5	Experience	Venkatesh and Bala 2008
	7	Impact	Rossi 2017
	8	Control	Marshman 2012
	9	Actual use	Venkatesh and Bala 2008; Cadwell
			et al. 2016
	12	Perceived usefulness	Venkatesh and Bala 2008
	13	Perceived usefulness	Venkatesh and Bala 2008
	14	Perceived ease of use	Venkatesh and Bala 2008
	15	Control	Marshman 2012
	16	Computer / CAT anxiety	Venkatesh and Bala 2008
	17	Subjective norm	Venkatesh and Bala 2008
	18	Image	Venkatesh and Bala 2008
	19	Job relevance	Venkatesh and Bala 2008
	20	Output quality	Venkatesh and Bala 2008
	22	Fear	Rossi 2017

relates questions to existing constructs and shows that we borrowed mostly from TAM3, and made very parsimonious additions as explained in the previous section.

Table 1 . Survey questions based on existing constructs.

 1

  -The translator "understands what a person says and makes it understandable to another" (Lederer)

	30% -I mostly disagree with this statement 4 or less 10. Which pattern of use best suits your expectations and conception of (35) -I cannot say / I have no opinion (8) 3% your job? -I mostly agree with this statement (9) 10 or less -Use MT in half of the cases (25) -I strongly agree with this statement (4) 27% -Never use MT (10) 10+ -Use MT in almost all cases (34) -I don't work in this translation environment (1)
	20+ -Other (please specify) 17. Influential people around me think I should use MT	(20)
	17% -I strongly disagree with this statement 23% 30+ 11. Which activities or tasks do you enjoy doing without using MT? -I mostly disagree with this statement	(7) (14)
	(Text box) -I cannot say / I have no opinion	(41)
	-I mostly agree with this statement	(20)
	12. I am more efficient when I use MT -I strongly agree with this statement	(7)
	-I strongly disagree with this statement	(6)
	-I mostly disagree with this statement 18. Translators with expertise and skills in MT and CAT tools have higher (7)
	-I cannot say / I have no opinion profile	(5)
	-I mostly agree with this statement -I strongly disagree with this statement	(42) (12)
	-I strongly agree with this statement -I mostly disagree with this statement	(28) (17)
	-NA -I cannot say / I have no opinion	(1) (33)
	-I mostly agree with this statement	(56) (19)
	-Other 13. I work better and the quality of the translations I write improves when (2) -I strongly agree with this statement (8)
	I use MT
	7. Among the changes that you have witnessed recently, would you say -I strongly disagree with this statement (11) 19. Machine translation is particularly well suited for the kind of translations
	that Machine Translation (henceforth MT): -I mostly disagree with this statement we handle at the DGT	(24)
	-Has had a major impact -I cannot say / I have no opinion I strongly disagree with this statement	(39) (15) (7)
	-Has not made a big difference -I mostly agree with this statement I mostly disagree with this statement	(32) (27) (15)
	-Other (please specify) -I strongly agree with this statement I cannot say / I have no opinion	(18) (11) (13)
	-NA I mostly agree with this statement	(1) (45)
	8. Would you say that MT affects: I strongly agree with this statement	(9)
	-The amount of work you do (Yes: 40 / No: 49) 14. It is easier for me to work with MT (both in terms of the tasks I handle
	-The kind of work you do (Yes: 17 / No: 72) and of cognitive load) 20. The quality of MT is generally usable
	-The quality of the work you do (Yes: 35 / No: 54) -I strongly disagree with this statement -I strongly disagree with this statement	(7) (8)
	-How you do your work --i.e. your working methods (Yes: 65 / No: 24) -I mostly disagree with this statement (8) -I mostly disagree with this statement (27)
	-Your relationship(s) with your client(s) and/or employer(s) -I cannot say / I have no opinion -I cannot say / I have no opinion	(7) (10)
	(Yes: 3 / No: 86) -I mostly agree with this statement -I mostly agree with this statement	(38) (38)
	-None of the above (2) -I strongly agree with this statement -I strongly agree with this statement	(28) (6)
	-NA	(1)
	9. When do you use MT? 20. What defines a usable MT output according to you?
	-Systematically and regardless of text type 15. I have more control over my work when I use MT (Text box)	(38)
	-Only in some cases -I strongly disagree with this statement	(41) (14)
	-Never -I mostly disagree with this statement 21. What does critical or sound use of MT mean to you?	(10) (31)
	-I cannot say / I have no opinion (Text box)	(11)
	9' (continued). If you do not use MT systematically, when do you use it? -I mostly agree with this statement (25)
	-Daily -I strongly agree with this statement 22. Do you feel that MT is:	(1) (7)
	-Weekly -NA -Very threatening	(19) (1) (2)
	-Monthly -Rather threatening	(11) (10)
	-A few times a year 16. My current translation environment (TRANSLATOR'S DESKTOP -CAT (10) -I cannot tell or have no opinion (13)
	Please specify if appropriate Client -Studio) does not require any major concentration effort -Rather helpful	(49)
	-I strongly disagree with this statement -Very helpful	(32) (15)

Fear of MT was assessed in terms of the degree of security that translators felt in relation to MT (see question 22 in Appendix 2). The lowest score thus indicated insecurity (if they felt that MT was "very threatening") and the highest corresponded to the secure feeling that MT was "very helpful".

"MT is the only viable solution for: quick and cheap access to information in foreign languages, understanding information received in a foreign language that otherwise could not be used or would require substantial time and costs to translate, making multilingual use of websites possible, facilitating cross-lingual information search and analytics."[START_REF] Pilos | How can public services benefit from the CEF.AT platform?[END_REF]

Acknowledgements

The research reported here was supported by ILCEA4 and conducted as part of an interdisciplinary project funded by the Pôle Grenoble Cognition (for more details on the project: https://evaluerlata.hypotheses.org/). We are also indebted to Élisabeth Lavault-Olléon, who suggested spending time with institutional translators and observing current practices, and helped us get everything started. Our warmest thanks to the DGT, for welcoming us and giving us permission to conduct our study. We also wish to thank the DGT's "Development of the translation profession" sector and to the EMT network for making this happen. None of the analyses presented here would have been possible without the active participation of DGT translators and heads of units, to whom we wish to express special thanks. The final version of the paper owns a lot to the anonymous reviewers and special issue editor, whose comments and suggestions were extremely accurate and helpful.

Appendix 1. Open questions used in semi-directed interviews (all conducted in French)

1.Quelle est votre formation ? Depuis combien de temps êtes-vous traducteur à la DGT ? What is your academic background and how long have you been working as a translator at the DGT? 2.Quelles sont vos principales sources de satisfaction et vos principales sources de frustration au travail ? Would you be able to explain where MT outputs come from and why they are not always reliable? 6.Comment pensez-vous que vous utiliserez les MT et la TA dans dix ans ? Cela peut vous amener à parler des évolutions que vous avez connues How do you think you will use TMs and MT in ten years? Feel free to discuss your own experience and the changes that you have witnessed.

Appendix 2. Survey questions (answers in brackets)

1. Which language department do you work in? (Answer chosen from list of ISO codes for all 24 departments: please see figure 5

Biographies

Caroline Rossi is a Lecturer involved in translator training at Université Grenoble Alpes, and a member of the ILCEA4 research team. She is a cognitive psycholinguist and her research interests range from first and second language acquisition to the epistemological, social and ergonomic consequences of current MT developments for both trainees and translators. E-mail: caroline.rossi@univ-grenoble-alpes.fr

The Journal of Specialised Translation Issue 31 -January 2019 Jean-Pierre Chevrot is a Professor at Université Grenoble Alpes and the head of the LIDILEM research team. His research topics include language acquisition, sociolinguistics and language in the social media. His concern is to bridge the gap between cognitive, social and linguistic approaches to language.

E-mail: jean-pierre.chevrot@univ-grenoble-alpes.fr

____________

The Journal of Specialised Translation Issue 31 -January 2019 1 MT@EC was replaced by eTranslation in November 2017, i.e. 10 months after the investigation presented in this paper, which is why we still refer to MT@EC. eTranslation has enabled a better integration into the Connecting Europe Facility (CEF), and an improved, quicker service. The engine itself is still based on MT@EC and mostly on the phrase-based statistical paradigm (neural machine translation has already been implemented for a few language pairs).


D Abdelkader
email: dhaferabdelkader@gmail.com

; F Chaffar Akkari

; N Khemiri

; R Miloua

; F Antoni

; B Gallas

; M Kanzari

Effect of SnS addition on the morphological and optical properties of (SnS) m (Sb 2 S 3 ) n nano-rods elaborated by glancing angle deposition

Keywords: Sn-Sb-S, GLAD, Nanostructured materials, Optical properties, morphological properties, X-ray diffraction

SnS) m (Sb 2 S 3 ) n thin films were prepared by thermal evaporation using the glancing angle deposition technique (GLAD). The incident angle between the particle flux and the normal to the substrate was fixed at 80°. The Raman and XRD characterization revealed the amorphous character of the films due to the columnar structure as shown by the SEM characterization and AFM analysis. A strong change of the surface morphology of the films was observed and it depends on the composition. Optical properties were extracted from transmittance T and reflectance R spectra. (SnS) m (Sb 2 S 3 ) n thin films exhibit high absorption coefficients (10 4 -2×10 5 cm -1 ) in the visible range and the higher values were obtained for Sn 3 Sb 2 S 6 and it has the highest photocurrent values. The direct band gap (E g dir ) was in the range 2.11 -1.67 eV.

The refractive indices are calculated from optical transmittance spectra of the films. The Sn 3 Sb 2 S 6 sample exhibits a lower refractive index. All the dispersion curves of refractive index match well with the Cauchy dispersion formula and they were analyzed using Wemple-DiDomenico model. The Bruggeman effective medium approximation EMA was used to calculate the packing density of different compositions, and SnSb 4 S 7 sample has the highest

Introduction

Glancing angle deposition (GLAD) technique has developed over the last few years. This technique is based on a physical deposition of vapor phase material on an oblique substrate with respect to the material flux. The substrate can be also moved according to two degrees of freedom (azimuthal or polar angle), as it can rotate around its axis. By acting on these parameters, nanostructures can be produced with controlled roughness and porosity [1][2][3][4].

This type of samples is in great demand to improve the sensing features of several devices.

One can site the gas sensors as an example. Moreover, by governing the freedom's degrees of the substrate, it is possible to perform several shapes by the GLAD technique, such as vertical columns [5], helical [6], zigzag shapes [7], U-shaped resonators [8], and even three-armed square nanospirals [9] .

For instance, films with a porous structure have been shown to operate well in various applications such as solar cell [10], photonic crystals [11], optical filters [12], gas sensors [13] and photo-catalysis [14]. It includes metallic nanostructures of Ag nanorods that exhibit potential plasmonic properties for biomedical applications [15]. Recent researches showed combined techniques with the GLAD in order to develop certain applications, since it was technologically very successful, such as Growth Assisted by Glancing Angle Deposition (GAGLAD), which is a new technique to engineer anisotropic hybrid inorganic/organic nanostructures thin films [16]. We can also find a combination with colloidal lithography to fabricate ordered tantalum nanotopographies [17] and in another work GLAD was combined with the collimated magnetron-sputtering technique in order to tailor the refractive index of

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3 the SiO 2 thin film [18]. Several oxides were deposited by GLAD including helical TiO 2 for perovskite solar cell application [19][20][21], hafnium oxide HfO 2 [22], LiCoO 2 nanostructured cathode for all-solid-state thin film batteries [23] and MgO for quarter-wave plates at a wavelength of 351 nm conception [24]. Aside from the oxides, chalcogenide materials class are among the materials that were performed using this technique. In a recent work, we showed optical anisotropy of CuIn 3 S 5 and CuIn 5 S 8 nanorods [25]. Pareek et al. [26] demonstrated that ZnCdS system is an efficient photoanodic material for hydrogen production. SnSe thin films exhibit enhanced thermoelectric properties when grown by pulsed laser glancing-angle deposition [27]. The effect of angle deposition of the binary materials SnS and Sb 2 S 3 was studied [28,29]. Ternary chalcogenide Sn-Sb-S materials belonging to the pseudo-binary system SnS-Sb 2 S 3 were not widely studied as nanostructures, and only the two phases Sn 4 Sb 6 S 13 and Sn 3 Sb 2 S 6 were deposited by GLAD [30,31]. These two previous works have only focused on the angle deposition effect. In the present work, we tried to investigate the effect of the variation of the chemical composition along the pseudo-binary system SnS-Sb 2 S 3 on the morphological, structural and optical properties. In order to achieve this, (SnS) m (Sb 2 S 3 ) n nanorod thin films were elaborated by GLAD with (m:n) equal to (1:2), (2:3), (1:1), (4:3), (2:1) and (3:1).

Experimental details

The last combinations of SnS and Sb 2 S 3 match the six following compositions SnSb 4 S 7 , Sn 2 Sb 6 S 11 , SnSb 2 S 4 , Sn 4 Sb 6 S 13 , Sn 2 Sb 2 S 5 and Sn 3 Sb 2 S 6 . Their powders were used for thermal evaporation, from a molybdenum boat, using a high vacuum coating unit Alcatel.

(SnS) m (Sb 2 S 3 ) n thin films were deposited at a base pressure of 10 -6 Torr using the GLAD technique. All the films were deposited onto unheated glass substrates, which were fixed at an oblique angle of 80 °. This angle value is considered as an extremely oblique position, in Transmittance and reflectance data were recorded at normal incidence with UV-Vis-NIR spectrophotometer. In order to calculate the packing density using the Bruggeman effective medium approximation (BEMA), the dielectric constant of the (SnS) m (Sb 2 S 3 ) n bulk materials was measured directly using UVISEL phase-modulated ellipsometer. The data measurement was performed at room temperature under a 70° incidence.

Results and discussion

Morphological properties

Fig. 1 shows the surface as well as the cross-section morphologies of the films. It can be seen that all the films show a columnar structure. This columnar structure results from great shadowing effect, which is enhanced for strong deposition angles, greater than 60 ° [30,31].

Obviously, the formation of this structure is strongly encouraged for an angle of 80 °. The slanted columns are highly arranged and inclined towards the evaporated material flux side.

The sorting of the materials according to the following order, SnSb 4 S 7 , Sn 2 Sb 6 S 11 , SnSb 2 S 4 , Sn 4 Sb 6 S 13 , Sn 2 Sb 2 S 5 and Sn 3 Sb 2 S 6 is a sorting in Sn content order. In another word, the sorting is in m/n ratio order, where m and n are the indices in the following formula

M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT 5 (SnS) m (Sb 2 S 3 ) n .
The taken values of m/n ratio are 1/2, 2/3, 1/1, 4/3, 2/1 and 3/1. The surface morphology shown in Fig. 1 depicts the porous structure of the films in SEM images taken at the same magnitude of 100 K×. SnSb 4 S 7 (m/n = 1/2) sample exhibits very small and compacted nanorods, and the film seems to be dense. We note that from m/n = 1/2 to 3/1 , the pores between the nanorods ends become more and more opened and the nanorods end have a larger size as can be seen in the surface morphology in SEM images. The AFM analysis comforts the observation seen in SEM images. The black surfaces shown by AFM images in Fig. 2.a depict the hollows situated at the surfaces and show how much the films are porous.

The analysis of the height distribution (Fig. 2.b) allows determining the pores threshold and the RMS roughness value (root mean square) and we found that they increase from 6.3 to 25 nm and from 1.6 to 5.3 nm, respectively (Table 1). Therefore, pores become deeper with SnS content, which enhances the surface roughness. A further important observation is that the entire series of elaborated films exhibit a thickness gradient as shown in 3D AFM images in Fig. 3, which is confirmed by SEM thickness measurement and Raman cartography. Therefore, at the top, the film is thicker. For widely oblique positions, the gradient of material flux is accentuated; the region of the substrate situated straight above the evaporation boat receives a greater flux. The diffusion on the surface is no longer the same due to this material flux gradient. Therefore, it is possible to fabricate thin films with a thickness gradient. In a previous work, tapered thickness films were elaborated using GLAD technique using a mask deposited on the substrate. The existence of the mask allows the creation of two regions; the first region is the shadowed zone of the substrate, and the second one is where the vapor flux completely reaches the substrate [32]. At the boundary of these regions, there is a small zone of thickness gradient [32]. In our experiment, the films are grown without using a mask, which is considered a technical advantage. This GLAD feature could be useful for nanophotonics and nanoelectronics applications [32].

M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT 6 3.

Structural properties

The XRD patterns of (SnS) m (Sb 2 S 3 ) n thin films deposited with GLAD are shown in Fig. 4 (a). As can be seen, there is no obvious diffraction peaks for SnSb 4 S 7 , Sn 2 Sb 6 S 11 , SnSb 2 S 4 and Sn 4 Sb 6 S 13 samples, indicating that films are amorphous. Only, the patterns of Sn 2 Sb 2 S 5 and Sn 3 Sb 2 S 6 thin films show weak peaks at 31.68° and 31.65°, respectively (JCPDS cards n° 44-0829 and 38-0826, respectively). We would note that these two compositions match with the highest values of m/n ratio.

It is well known that the glancing angle deposition technique does not conserve good crystalline features for great deposition angles [28] and in some cases the films become amorphous [33]. It was shown that Sn-Sb-S ternary materials are among these cases especially when the deposition is at room temperature [30,31]. The amorphous character of our films is due to the porous nanostructure. The void between the nanorods contributes to the amorphous behavior because we have demonstrated in a previous work that the normal deposited (SnS) m (Sb 2 S 3 ) n films are polycrystalline in nature, without any heat treatment [34].

In order to explain the trend of Sn 2 Sb 2 S 5 and Sn 3 Sb 2 S 6 to have a polycrystalline structure, we compared the heat of atomization H s of these materials with each other and with the ones of its elementary compounds Sn and Sb. For ternary materials A x B y C z , H s is expressed as [35]:

A B C s s s s xH yH zH H
x y z

+ + = + + (1)
where x, y, and z are the ratios of A(Sn), B(Sb), and C(S), respectively. From the To consolidate this idea, we refer to the work of Sazideh et al. [28] on the effect of deposition angle on SnS thin films, since Sn 2 Sb 2 S 5 and Sn 3 Sb 2 S 6 are the closest compositions to SnS in the pseudo-binary system SnS-Sb 2 S 3 . They demonstrated that the intensity of the main XRD peaks decreased but the films were still well crystallized even at high deposition angles and without any heat treatment [28]. which is in accordance with XRD results. Besides, the surface state contributes to the change in the shape of the Raman spectra especially when the surface exhibits a roughness and porosity due to the columnar structure. Generally, the physical state, the morphology, thermal properties, homogeneity and pores distribution, would all be able to influence how a sample is exhibited to the Raman spectrometer [START_REF] Smith | Modern Raman Spectroscopy -A Practical Approach[END_REF]. The Raman spectra of highly crystallized materials resemble more closely the Raman spectra associated with their molecular crystals [START_REF] Bougeard | Infrared and Raman spectroscopy: methods and applications[END_REF].

The effect of SnS addition is notable on the Raman spectra of the normally deposited films; the intensity of some peaks perceptibly increases, especially the one located at 94 cm -1 assigned to [SnS 4 ] tetrahedral unit [START_REF] Abdelkader | [END_REF]. This effect is not apparent in the spectra of the GLAD sample because the thickness affects the intensity and this is confirmed by Raman cartography. In other words, since the samples produced by GLAD have a thickness gradient, it is difficult to look for the same thickness for the different films, in order to make the Raman measurements. To highlight this, Raman cartography was carried out; the measurements were taken simultaneously on 10 aligned points, 10 µm apart, in the thickness gradient. For the

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same wavenumber, the intensity decreases as a function of the X position on the direction of the thickness gradient (Fig. 5).

All the Raman spectra of (SnS) m (Sb 2 S 3 ) n films were well fitted using seven Gaussian functions as shown in Fig. 6. Lorentzian fit attempts fail to regenerate the experimental Raman spectra. The fitting analysis parameters of the experimental Raman spectra are summarized in Table 2.

Optical properties of (SnS) m (Sb 2 S 3 ) n films 3.3.1 Transmittance and reflectance spectra

The transmittance spectra of (SnS) m (Sb 2 S 3 ) n nanorods are shown in Fig. 7 (a). The values are in the range 60-90 % in the transparency region and all spectra show a sharp fall at the band edge. All the transmittance spectra show loose oscillations and weak amplitude for λ > 690 nm. Oscillations of the transmittance spectra of the normally deposited films were tighter and exhibit stronger amplitudes [34]. The amplitude decreases when the deposition angle becomes higher, and this is a well-known feature of the GLAD technique, found by other works [7,[29][30][31]. The transmittance of Sn 3 Sb 2 S 6 sample is the highest, and the one of SnSb 4 S 7 is the lowest. However, the reflectance varies inversely, as Fig. 7 (a) makes clear, SnSb 4 S 7

sample has the highest reflectance and Sn 3 Sb 2 S 6 has the lowest one. This may be due to the surface morphologies because the confinement of light is greater when it reaches large pores.

Therefore, the light trapping is enhanced.

Absorption coefficients and optical band gaps

The absorption coefficients α of such absorbing films can be expressed in terms of the transmittance T and reflectance R data using the relation [34]:

1 (1 )² ( ) R ln d T α - = ( 2
)
where d is the thickness of the film. It can be seen that the absorption coefficient α increases gradually and it exceeds 10 4 cm -1 . Fig. 7 (b) shows that all the (SnS) m (Sb 2 S 3 ) n films show

M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT 9
high absorption coefficients between 10 4 and 2×10 5 cm -1 in the visible range and the higher values were obtained for Sn 3 Sb 2 S 6 . This is a vital outcome because semiconducting nanostructures have developed as a promising course toward accomplishing high efficiencies in photovoltaic solar cells [39]. Recently, many works demonstrated that crystalline nanostructures exhibit a high-efficiency solar cell [40,41]. The Sn-Sb-S materials do not require high annealing temperature to be crystallized, so it would be efficient to elaborate these materials as crystalline nanostructures. To obtain detailed information about the energy band gap of the samples, the dependence of the absorption coefficients α on the photon energy is analyzed in the sharp absorption region using the Tauc relation which has to date been the most popular heuristic expression, correlating the absorption coefficient and the optical band gap E g . It can be expressed as [42]:

( ) g h h E γ α ν ν ∝ - (3)
In this relation, γ = 1/2 for a direct allowed transition or γ = 2 for an indirect allowed transition. The extrapolation to zero absorption with the photon energy axis of the linear part of (αhν) 2 and (αhν) addition. We note that band gap values of the samples elaborated by glancing angle deposition are higher than the ones normally deposited in a previous work [34]. The increase of the band gap value is a famous feature of the samples elaborated by GLAD technique, due to the increased disorder and porous character of the films. We have looked for the causes of gap redshift on chalcogenide materials in the literature, and we found two main reasons that are the increase of any metal content [43][44][START_REF] Pradhan | AIP Conf. Proc[END_REF][START_REF] Alvi | [END_REF]. Consequently, the increase of the homopolar bonds between the atoms of the same metal. In order to comfort this idea, we compared with the band gap variation of a similar ternary system Ge-As-Se, where both the increase of Ge content (increase in Ge/As ratio) and UV exposure cause a reduction in E g [47]. In our case, the SnS addition (increase in m/n ratio) increases the [SnS 4 ] units, and therefore increase of the homopolar bonds Sn-Sn, which causes an upward shift to the conduction band bottom.

The extinction coefficient k = αλ/4π reflects the amount of intensity loss of a given electromagnetic wave propagating through any medium. Therefore, the related electric field having a wavelength lower than 800 nm will be enormously dissipated (Fig. 7 (d)). For wavelengths higher than 800 nm, the extinction is practically null so the light wave can get through the film several times and thus interference phenomena is produced in the transparency region. This characteristic makes the samples useful for photonic devices in this spectral region. Below this region, the change in the extinction coefficient is due to the bandto-band excitation [48].

Some photovoltaic parameters:

Using

where, d is the thickness (in nm), F(λ) is the wavelength-dependent AM1.5G photon flux, e is the electron charge and λ g =1240/E g . In Fig. 8 we compare the performance of our thin films and show that among the six phases, Sn ) exhibit a similar photovoltaic performance.

Refractive indices and dispersion analysis

The refractive index n is an essential property for any optical material. It is firmly associated to the electronic polarization and the nearby field of atoms or molecules inside these materials. In this way, the knowledge of refractive index is impressively vital, particularly for the materials that can be useful for the manufacture of any optical gadgets. It can be calculated from the Swanepoel method, which requires at least two interference fringes in the transmittance spectrum [51].

Fig. 9 (a) illustrates the refractive indices of (SnS) m (Sb 2 S 3 ) n thin films estimated using the Swanepoel method. The four terms in the Cauchy relation (n = n 0 +A/λ 2 +B/λ 4 +C/λ 6 ) allow the determination of a good fit shape of the refractive index in terms of normal dispersion at very weak absorption domain (>800 nm). The Cauchy parameters are classified in Table 3. We note that Sn 3 Sb 2 S 6 has the lower value of refractive index n. Hence it can be concluded from this work and the two previous works [34,[START_REF] Abdelkader | [END_REF] that the refractive index of Sn 3 Sb 2 S 6 is still the

M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT 12
smallest among those of the other five thin-film compositions, deposited on glass or on silicon or by GLAD.

The shape of the refractive indices displayed two main parts. The part I shows higher values above the absorption edge. In this part, the refractive index exhibits an abnormal dispersion behavior. This anomalous result is ascribed to the plasma frequency ω p of the photon energy [52]. It is clear in Fig. 9 (a) that this anomaly depends on the SnS content (m/n ratio); it appears logical since the anomalous dispersion occurs at the absorption edge. In the 2 nd part (> 750 nm), the refractive index of different films (SnS) m (Sb 2 S 3 ) n shows a normal dispersion. A similar behavior was reported for chalcogenide materials including SnS [48],

GeSe 2-x Sn x [53], InSe [54] and Sb 2 S 3 [55]. Naturally, the SnS addition creates a bonding change and therefore a polarizability change of the material, which is directly linked to the refractive index via Lorentz-Lorenz relation. Besides, the density of the material is affected by SnS addition. The changes of the polarizability and the material density can cancel each other in such a way that refractive index remains unchanged.

In the region of normal dispersion (region II), the model proposed by Wemple and DiDomenico [56] which is a single oscillator model could be applied. In this model, the refractive index curve has been analyzed beneath the inter-band absorption edge. The energy dependence of the refractive index satisfies the following relation :

2 0 2 0 1 1 ( ) 1 d d E h n E E E ν = - × - (6)
where E 0 is the oscillator energy and E d is the dispersion energy parameter of the material that measures the strength of inter-band optical transitions. Plots of (n 2 -1) -1 versus (hν) 2 of the investigated compositions can be linearly fitted in the applicability zone of the Wemple-DiDomenico, and are given in Fig. 9 (b). For each plot, the fit allows the estimate of the values of both E 0 and E d , given in Table 3.

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Different effective medium theories are usually used to describe the dielectric constant of a composite material, which provide relationships between the packing density of the thin film and the dielectric constant. The effective medium approximation [57] is the most commonly used theory. Since its expectations are typically sensible and physically offer methods for quick knowledge into some issues that are hard to assault by different methodologies. The packing density p is a significant characteristic of the (SnS) m (Sb 2 S 3 ) n GLAD films. It can be calculated based on the effective medium approximation, and thus using the mixture rule proposed by Bruggeman:

( ) ( ) 0 2 2 a eff b eff a b a eff b eff p p ε ε ε ε ε ε ε ε - - + = + + (7)
where ε a and ε b are the dielectric constants of a and b components. These components are randomly distributed in space with a packing density of p a and p b , respectively (p a + p b = 1).

The dielectric properties of the medium are then described by the effective dielectric constant ε eff . For our films, we considered that a component is the (SnS) m (Sb 2 S 3 ) n bulk material, its dielectric constant ε a was measured directly by ellipsometry, and b component is the void. As a result, we considered ε eff as the dielectric constant of the film. In addition, from the optical constants n and k of the GLAD films at 800 nm, packing density of different samples have systematically been calculated. Note that SnSb 4 S 7 film exhibits the highest value of the packing density and Sn 3 Sb 2 S 6 the lowest one, which is in agreement with the SEM observations. Let's remember that these two compositions correspond to the two extreme ratio m/n in the (SnS) m (Sb 2 S 3 ) n formula. Usually, the packing density and the effective refractive index decrease with increasing deposition angle [30,31]. In this work, the deposition angle is fixed but the packing density depends on the composition.

Magneto-optical constant: Verdet coefficient

V M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT 14
These samples are transparent in the near infrared domain, as it is clear from the transmission spectra (Fig. 7(a)). Therefore, they could be used as windows for applications based on magneto-optical measurements in this spectral range. The Faraday rotation is an important quantity for such measurements, and it depends on the Verdet coefficient V given by the following relation [58]:

0 ( ) 2 e dn V µ r mc d λ λ λ =
where µ 0 is the vacuum permeability, r is the magneto-optical anomaly factor, e is the electron charge, m is the electron mass, and n is the refractive index of the material, dn/dλ is the dispersion of the material. Usually, for the material comprised for the most part of the covalent kind bond, r is near 0.28, and for the material mainly comprised of the ionic kind bond, r is near 1 [58]. The Verdet coefficient is normally measured using two Glan-Taylor polarizers [59,60], but in our study, we estimated it from the refractive index dispersion. We note that the Verdet coefficient takes positive and negative values, and are mainly dependent on the band gap in the negative part (Fig. 10). This result was found for Cd-Mn-Fe-Te system, where the Verdet dispersion was negative and enhanced near the fundamental band gap energy because of the sp-d exchange interaction is enhanced [59]. In addition, in a reported work on the Tb 2 Sn 2 O 7 crystal, the Verdet constant exhibits negative values [61]. In general, the amount of heavy metals present in the sample increases the Verdet coefficient [58]. Since

V depends on the optical dispersion, polarization and the energy splitting on the ion energy levels, the Sn addition effect in the ternary under study may be canceled by the antimony polarizability.

Conclusions

In this work, we investigated the structural, morphological and optical properties of the (SnS) m (Sb 2 S 3 ) n thin films prepared by glancing angle deposition GLAD using a single thermal

Table 2:

The fitting analysis parameters of the experimental Raman spectra.

Table 3: Cauchy parameters n 0 , A and B, Wemple-DiDomenico parameters the oscillator energy E 0 , dispersion energy parameter E d .

Table 4:

The values of dielectric constant ε eff of GLAD samples, ε bulk of the bulk at λ = 800 nm and the corresponding packing density.

  the formation of columns. In our experiment, the deposition angle was fixed without substrate rotation.The roughness, morphology and cross-sectional features of the samples were examined by atomic force microscope (AFM) (NT-MDT Smena) and scanning electron microscopy (SEM) (ZEISS Supra 40). The structural properties were determined by the X-ray diffraction technique using a Philips X'Pert X-ray diffractometer (40 kV, 30 mA, and CuK α radiation λ = 0.15418 nm) in the range of 2θ = 10 -70 ° at room temperature. Raman scattering measurements were carried out at room temperature with a Horiba Lab-RAM Aramis confocal Raman spectrometer (λ excitation = 532 nm) equipped with a cooled CCD camera and an automated XYZ table. An objective lens of 100× magnification and D3 filter were used.

7 cause

 7 of the appreciable change of the surface morphology as it can be seen in Fig.1.

Fig. 4 (

 4 Fig.4 (b) shows the Raman spectra of (SnS) m (Sb 2 S 3 ) n films deposited by GLAD and

  photo-induced change or a change in chemical composition that favors

  the measured absorption coefficient α(λ), we estimated the absorbed photon fraction and photocurrent of the deposited thin films. Assuming (SnS) m (Sb 2 S 3 ) n based solar cell with perfect antireflection, no absorption in the upper layers and normal incidence, the thicknessdependent absorbed photon fraction and the photocurrent is calculated by the following relations[49,50] :

  a vacuum. The substrates were maintained at an angle of flux incidence of 80 °. SnSb 4 S 7 , Sn 2 Sb 6 S 11 , SnSb 2 S 4 and Sn 4 Sb 6 S 13 films were firmly amorphous and Sn 2 Sb 2 S 5 and Sn 3 Sb 2 S 6 exhibit weak peaks of XRD, but their crystallinity was considered very poor. The shadowing effects and limited adatom diffusion, which inhibits the diffusivity of deposited atoms at room temperature, were the most probable cause of the amorphous character of the samples. The surface morphology was strongly dependent on the composition. The (SnS) m (Sb 2 S 3 ) n thin films had high absorption coefficients which reach 2×10 5 cm -1 in the visible range and the higher values were obtained for Sn 3 Sb 2 S 6 . The shifting of the absorption edge of the films to the lower photon energy region depended on the SnS addition, and this caused a decrease of the direct band gap (E g dir ) and indirect band gap (E g ind ) from 2.11 to 1.67 eV and from 1.87 to 1.42 eV, respectively. Cauchy relation and Wemple-DiDomenico model analyzed the dispersion curves of the refractive index. The Bruggeman effective medium approximation was used to estimate the packing density p of different films that was determined using values of the refractive index at 800 nm. The maximum of the packing density was attributed to the SnSb 4 S 7 (minimum of m/n) and the minimum one was for Sn 3 Sb 2 S 6 (maximum of m/n). The absorbed photo-fraction and the photocurrent were calculated for all the samples. Sn 3 Sb 2 S 6 exhibited the most important photocurrent values among the samples under study. Finally, the Verdet coefficient of the different specimens was calculated from the refractive index dispersion and it exhibited a spread negative range, which was dependent on the band gap energy.

Fig. 1 :

 1 Fig.1: Cross-sectional (left) and surface morphology (right) SEM images of (SnS) m (Sb 2 S 3 ) n glancing angle deposited films.

Fig. 2 :

 2 Fig.2: (a) AFM topographic and porosity threshold images and (b) roughness histograms of (SnS) m (Sb 2 S 3 ) n films.

Fig. 3 :

 3 Fig.3: Schematic illustration of the thickness gradient films elaboration.

Fig. 4 :

 4 Fig.4: (a) XRD patterns and (b) Raman spectra of (SnS) m (Sb 2 S 3 ) n glancing angle deposited films compared with (c) Raman spectra of (SnS) m (Sb 2 S 3 ) n normally deposited.

Fig. 5 :

 5 Fig.5: Line focus Raman cartography and the variation of the intensity at 105 cm -1 as a function of X position taken in the direction of the thickness gradient.

Fig. 6 :

 6 Fig.6:The Gaussian decomposition of the Raman spectra of (SnS) m (Sb 2 S 3 ) n glancing angle deposited films.

Fig. 7 :

 7 Fig.7: (a) Reflectance and transmittance (b) absorption coefficients (c) (αhν) 2 and (d) extinction coefficients spectra of (SnS) m (Sb 2 S 3 ) n glancing angle deposited films.

Fig. 8 :

 8 Fig.8: (a) Absorbed photo fraction and (b) photocurrent vs film thickness.

Fig. 9 :Fig. 10 :

 910 Fig.9: (a) Refractive indices and Cauchy fit and (b) plots of (n 2 -1) -1 versus photon energy squared (hν) 2 of (SnS) m (Sb 2 S 3 ) n glancing angle deposited films.Fig.10: Verdet coefficient dispersion of (SnS) m (Sb 2 S 3 ) n glancing angle deposited films.

  Fig.1

  Fig.3

  Fig.5

  Fig.8

Fig. 10 450

 10 Fig.10

Table 1 ,

 1 it is clear that the average heat of atomization H s increases from 275.25 to 282.18 kJ.mol -1 with an increase in Sn content. Sn 2 Sb 2 S 5 and Sn 3 Sb 2 S 6 have the highest values of Sn content and exist in the Sn-rich side of the pseudo-binary SnS-Sb 2 S 3 system. Its atomization energies

trends to the one of Sn, which is a highly crystalline metal. This is the most probable cause of the trend of Sn 2 Sb 2 S 5 and Sn 3 Sb 2 S 6 to have a polycrystalline structure. It may also be the

Table 1 .

 1 The sample Sn 3 Sb 2 S 6 (the maximum of the ratio m/n) has the lowest value of band gap energy. However, SnSb 4 S 7 (the minimum of the ratio m/n) has the highest one. Therefore, the red shift of the band gap results from SnS

1/2

plots allows the determination of the direct and indirect band gaps, respectively (Fig.

7 (c)

). (SnS) m (Sb 2 S 3 ) n samples exhibit direct and indirect gaps. The direct band gap (E g dir ) and indirect band gap (E g ind ) are in the range 2.11-1.67 eV and 1.87-1.42 eV, respectively and are summarized in

  3 Sb 2 S 6 exhibits the best performance with relative optical absorption very close to 97 % and photocurrent better than 32.7 mA/cm² for layer thicknesses greater than 3 µm. In addition, SnSb 4 S 7 and Sn 2 Sb 6 S 11 (resp. SnSb 2 S 4 and Sn 4 Sb 6 S 13

Table 1 :

 1 Heat of atomization, RMS, threshold, number of pores in a sample surface 1 µm² and values of direct and indirect band gap energy of (SnS) m (Sb 2 S 3 ) n materials deposited by GLAD.

Table 1 :

 1 Heat of atomization, RMS, threshold, number of pores in a sample surface 1 µm² and values of direct and indirect band gap energy of (SnS) m (Sb 2 S 3 ) n materials deposited by GLAD.

		Sn	302
		Sb S	262 279	RMS (nm)	Threshold (nm)	Number of pores in 1 µm²	E g dir (eV)	E g ind (eV)
	1/2	SnSb 4 S 7	275.25	1.64	6.370	218	2.11	1.87
	2/3	Sn 2 Sb 6 S 11	276.05	1.71	7.434	219	2.09	1.86
	1/1	SnSb 2 S 4	277.43	1.73	8.316	264	2.05	1.82
	4/3	Sn 4 Sb 6 S 13	278.57	1.71	9.986	307	2.04	1.81
	2/1	Sn 2 Sb 2 S 5	280.33	3.96	15.339	107	1.70	1.42
	3/1	Sn 3 Sb 2 S 6	282.18	5.35	25.419	392	1.67	1.48

Table 2 :

 2 The fitting analysis parameters of the experimental Raman spectra.

	Composition Peak	Area (a. u×cm -1 )	Peak position (cm -1 )	Width W (cm -1 )	Baseline offset (a. u)
		1	74,683	295.89	99.989
		2	5,907.2	220.87	53.272
		3	15,803	125.51	40.078
	SnSb 4 S 7	4	14,620	165.45	49.354	115.29
		5	11,418	96.16	24.564
		6	7,070.2	77.88	15.790
		7	2,884.7	67.69	10.800
		1	4,797.6	323.76	44.344
		2	26,661	309.74	85.132
		3	17,560	277.82	74.323
	Sn 2 Sb 6 S 11	4	19,860	233.45	111.360	188.85
		5	21,084	135.50	59.304
		6	10,978	94.64	27.601
		7	6,002.6	74.18	14.869
		1	18,637	313.01	18637
		2	14,193	292.56	90.668
		3	14,211	246.88	79.087
	SnSb 2 S 4	4	9,329.6	158.56	52.207	138.82
		5	9,772.3	119.68	42.879
		6	5,972.6	94.89	22.244
		7	5,434.2	74.69	15.886
		1	26,279	312.44	67.382
		2	14,862	246.10	61.034
		3	10,499	160.23	59.787
	Sn 4 Sb 6 S 13	4	5,919.0	121.99	37.199	164.18
		5	5,608.6	97.69	21.268
		6	3,267.2	80.51	13.879
		7	2,303.3	70.44	10.924
		1	23,634	306.00	81.498
		2	10,780	227.73	51.489
		3	3,920.0	182.19	27.675
	Sn 2 Sb 2 S 5	4	7,789.3	148.78	36.874	108.78
		5	6,090.3	112.62	30.759
		6	5,748.6	92.02	19.580
		7	3,586.1	72.49	13.048
		1	27,049	296.20	86.393
		2	9,254.4	217.12	46.822
		3	1,915.7	183.31	17.692
	Sn 3 Sb 2 S 6	4	11,685	152.31	42.175	115.431
		5	5,583.1	113.04	29.180
		6	6,512.5	91.84	18.037
		7	3,657.2	72.35	12.606

Table 3 :

 3 Cauchy parameters n 0 , A, B and C, Wemple-DiDomenico parameters the oscillator energy E 0 , dispersion energy parameter E d .

	Materials	ε eff (λ=800 nm)	ε bulk (λ=800 nm)	p
	SnSb 4 S 7	6.295	6.766	0.942
	Sn 2 Sb 6 S 11	5.480	6.325	0.888
	SnSb 2 S 4	5.073	7.943	0.721
	Sn 4 Sb 6 S 13	5.631	6.084	0.936
	Sn 2 Sb 2 S 5	6.317	7.697	0.857
	Sn 3 Sb 2 S 6	4.269	8.607	0.622

Table 4 :

 4 The values of dielectric constant ε eff of GLAD samples, ε bulk of the bulk at λ = 800 nm and the corresponding packing density.

SnSb 4 S 7 Sn 2 Sb 6 S 11 SnSb 2 S 4 Sn 4 Sb 6 S 13 Sn 2 Sb 2 S 5 Sn 3 Sb 2 S 6

-1 ) SnSb 4 S 7 Sn 4 Sb 6 S 13 Sn 2 Sb 6 S 11 Sn 2 Sb 2 S 5 SnSb 2 S 4 Sn 3 Sb 2 S 6

	(a)
	Intensity (a. u.)
	Bragg Angle 2θ θ θ θ (°)

SnSb 4 S 7 Sn 4 Sb 6 S 13 Sn 2 Sb 6 S 11 Sn 2 Sb 2 S 5 SnSb 2 S 4 Sn 3 Sb 2 S 6 (c) Intensity (a. u.) Raman shift (cm -1 ) SnSb 4 S 7 Sn 2 Sb 6 S 11 SnSb 2 S 4 Sn 4 Sb 6 S 13 Sn 2 Sb 2 S 5 Sn 3 Sb 2 S 6

SnSb 4 S 7 Sn 2 Sb 6 S 11 SnSb 2 S 4 Sn 4 Sb 6 S 13 Sn 2 Sb 2 S 5 Sn 3 Sb 2 S 6

	α α α α (cm -1 )	(b)
	Absorption coefficient
		Photon energy hν ν ν ν (eV)

SnSb 4 S 7 Sn 2 Sb 6 S 11 SnSb 2 S 4 Sn 4 Sb 6 S 13 Sn 2 Sb 2 S 5 Sn 3 Sb 2 S 6

	(c)
	(α α α αhν ν ν ν)

2

(eV/cm) 2 Photon energy hν ν ν ν (

eV) SnSb 4 S 7 Sn 2 Sb 6 S 11 SnSb 2 S 4 Sn 4 Sb 6 S 13 Sn 2 Sb 2 S 5 Sn 3 Sb 2 S 6 (d) SnSb 4 S 7 Sn 2 Sb 6 S 11 SnSb 2 S 4 Sn 4 Sb 6 S 13 Sn 2 Sb 2 S 5 Sn 3 Sb 2 S 6 Extinction coefficient k

	Wavelength λ λ λ λ (nm)

2 (eV 2 ) SnSb 4 S 7 Sn 2 Sb 6 S 11 SnSb 2 S 4 Sn 4 Sb 6 S 13 Sn 2 Sb 2 S 5 Sn 3 Sb 2 S 6 Linear fit 400 600 800 1000 1200 1400 1600 1800 2000

	3.0 3.1	I	II
	2.9
	2.8
	2.7
	2.6
	2.5
	2.4
	2.3
	2.2
	2.1
	2.0
	1.9
	1.8	(a)
	1.7

Refractive index n Wavelength (nm) SnSb 4 S 7 Sn 2 Sb 6 S 11 SnSb 2 S 4 Sn 4 Sb 6 S 13 Sn 2 Sb 2 S 5 Sn 3 Sb 2 S 6 Cauchy fit

Acknowledgment

The authors wish to express their gratitude to Dominique Demaille for SEM characterization at Institut des NanoSciences de Paris (INSP) and to Jérémy Bartringer at ICube-Laboratoire des Sciences de l'Ingénieur, de l'Informatique et de l'Imagerie-France for Raman measurement.

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