International audience

In an era of rapid technological advancements, communication continues to evolve as new linguistic phenomena emerge. Among these is Arabizi, a hybrid form of Arabic that incorporates Latin characters and numbers to represent the spoken dialects of Arab communities. Arabizi is Widely used on social media and allows people to communicate in an informal and dynamic way, but it poses significant challenges for machine translation due to its lack of formal structure and deeply embedded cultural nuances. This case study is motivated by a growing need to translate Arabizi for gisting purpose. It evaluates the capacity of different LLMs’ to decode and translate Arabizi, focusing on multiple Arabic dialects that have rarely been studied up until now. Using a combination of human evaluators and automatic metrics, this research project investigates the model’s performance in translating Arabizi into both Modern Standard Arabic and English. Key questions explored include which dialects are translated most effectively and whether translations into English surpass those into Arabic.

Pictographs have been found to improve patient comprehension of medical information or instructions. However, tools to produce pictograph representations from natural language are still scarce. In this contribution we describe a system that automatically translates French speech into pictographs to enable diagnostic interviews in emergency settings, thereby providing a tool to overcome the language barrier or provide support in Augmentative and Alternative Communication (AAC) contexts. Our approach is based on a semantic gloss that serves as pivot between spontaneous language and pictographs, with medical concepts represented using the UMLS ontology. In this study we evaluate different available pre-trained models fine-tuned on artificial data to translate French into this semantic gloss. On unseen data collected in real settings, consisting of questions and instructions by physicians, the best model achieves an F0.5 score of 86.7. A complementary human evaluation of the semantic glosses differing from the reference shows that 71% of these would be usable to transmit the intended meaning. Finally, a human evaluation of the pictograph sequences derived from the gloss reveals very few additions, omissions or order issues (<3%), suggesting that the gloss as designed is well suited as a pivot for translation into pictographs.

Pretrained Language Models (PLMs) are the de facto backbone of most state-of-the-art NLP systems. In this paper, we introduce a family of domain-specific pretrained PLMs for French, focusing on three important domains: transcribed speech, medicine, and law. We use a transformer architecture based on efficient methods (LinFormer) to maximise their utility, since these domains often involve processing long documents. We evaluate and compare our models to state-of-the-art models on a diverse set of tasks and datasets, some of which are introduced in this paper. We gather the datasets into a new French-language evaluation benchmark for these three domains. We also compare various training configurations: continued pretraining, pretraining from scratch, as well as single- and multi-domain pretraining. Extensive domain-specific experiments show that it is possible to attain competitive downstream performance even when pre-training with the approximative LinFormer attention mechanism. For full reproducibility, we release the models and pretraining data, as well as contributed datasets.

The RCnum project is funded by the Swiss National Science Foundation and aims at producing a multilingual and semantically rich online edition of the Registers of Geneva Council from 1545 to 1550. Combining multilingual NLP, history and paleography, this collaborative project will clear hurdles inherent to texts manually written in 16th century Middle French while allowing for easy access and interactive consultation of these archives.

This paper presents a comparative analysis between human translation (HT) and post-edited machine translation (PEMT) from a lexical and syntactic perspective to verify whether the tendency of neural machine translation (NMT) systems to produce lexically and syntactically poorer translations shines through after post-editing (PE). The analysis focuses on three datasets collected in professional contexts containing translations from English into French and German into French. Through a comparison of word translation entropy (HTRa) scores, we observe a lower degree of lexical diversity in PEMT compared to HT. Additionally, metrics of syntactic equivalence indicate that PEMT is more likely to mirror the syntactic structure of the source text in contrast to HT. By incorporating raw machine translation (MT) output into our analysis, we underline the important role post-editors play in adding lexical and syntactic diversity to MT output. Our findings provide relevant input for MT users and decision-makers in language services as well as for MT and PE trainers and advisers.

PROPICTO is a project funded by the French National Research Agency and the Swiss National Science Foundation, that aims at creating Speech-to-Pictograph translation systems, with a special focus on French as an input language. By developing such technologies, we intend to enhance communication access for non-French speaking patients and people with cognitive impairments.

We present the PASSAGE project, which aims at automatic Standard German subtitling of Swiss German TV content. This is achieved in a two step process, beginning with ASR to produce a normalised transcription, followed by translation into Standard German. We focus on the second step, for which we explore different approaches and contribute aligned corpora for future research.

The past few years have seen the multiplication of studies on post-editese, following the massive adoption of post-editing in professional translation workflows. These studies mainly rely on the comparison of post-edited machine translation and human translation on artificial parallel corpora. By contrast, we investigate here post-editese on comparable corpora of authentic translation jobs for the language direction English into French. We explore commonly used scores and also proposes the use of a novel metric. Our analysis shows that post-edited machine translation is not only lexically poorer than human translation, but also less dense and less varied in terms of translation solutions. It also tends to be more prolific than human translation for our language direction. Finally, our study highlights some of the challenges of working with comparable corpora in post-editese research.

In Switzerland, two thirds of the population speak Swiss German, a primarily spoken language with no standardised written form. It is widely used on Swiss TV, for example in news reports, interviews or talk shows, and subtitles are required for people who cannot understand this spoken language. This paper focuses on the task of automatic Standard German subtitling of spoken Swiss German, and more specifically on the translation of a normalised Swiss German speech recognition result into Standard German suitable for subtitles. Our contribution consists of a comparison of different statistical and deep learning MT systems for this task and an aligned corpus of normalised Swiss German and Standard German subtitles. Results of two evaluations, automatic and human, show that the systems succeed in improving the content, but are currently not capable of producing entirely correct Standard German.

We believe that machine translation (MT) must be introduced to translation students as part of their training, in preparation for their professional life. In this paper we present a new version of the tool called MT3, which builds on and extends a joint effort undertaken by the Faculty of Languages of the University of Córdoba and Faculty of Translation and Interpreting of the University of Geneva to develop an open-source web platform to teach MT to translation students. We also report on a pilot experiment with the goal of testing the viability of using MT^3 in an MT course. The pilot let us identify areas for improvement and collect students’ feedback about the tool’s usability.

In diagnostic interviews, elliptical utterances allow doctors to question patients in a more efficient and economical way. However, literal translation of such incomplete utterances is rarely possible without affecting communication. Previous studies have focused on automatic ellipsis detection and resolution, but only few specifically address the problem of automatic translation of ellipsis. In this work, we evaluate four different approaches to translate ellipsis in medical dialogues in the context of the speech to speech translation system BabelDr. We also investigate the impact of training data, using an under-sampling method and data with elliptical utterances in context. Results show that the best model is able to translate 88% of elliptical utterances.

This paper presents the preliminary results of an ongoing academia-industry collaboration that aims to integrate MT into the workflow of Swiss Post’s Language Service. We describe the evaluations carried out to select an MT tool (commercial or open-source) and assess the suitability of machine translation for post-editing in Swiss Post’s various subject areas and language pairs. The goal of this first phase is to provide recommendations with regard to the tool, language pair and most suitable domain for implementing MT.

The project ‘Proposal and Implementation of a Swiss Research Centre for Barrier-free Communication’ (BFC) is a four-year project (2017–2020) funded by the Rectors’ Conference of Swiss Higher Education Institutions (swissuniversities).1 Its purpose is to ensure that individuals with a visual or hearing disability, people with a temporary cognitive impairment and speakers without sufficient knowledge of local languages can communicate and enjoy barrier-free access to information in all spheres of life, with a special focus on higher education.

She had a fundamental role in the founding and successful development of SIGDAT.Susan arrived in Switzerland from the United States in 1978 to work at the University of Lausanne in the German Department teaching literature and linguistics, as well as pursuing her interest in computers and language as translator and consultant to Logitech.She then moved to Geneva to work at the ISSCO research institute where she participated in a number of European research projects related to natural language processing (NLP) and machine translation, also working with corpora in translation

We describe how we were able to improve the accuracy of a medium-vocabulary spoken dialog system by rescoring the list of n-best recognition hypotheses using a combination of acoustic, syntactic, semantic and discourse information. The non-acoustic features are extracted from different intermediate processing results produced by the natural language processing module, and automatically filtered. We apply discriminative support vector learning designed for re-ranking, using both word error rate and semantic error rate as ranking target value, and evaluating using five-fold cross-validation; to show robustness of our method, confidence intervals for word and semantic error rates are computed via bootstrap sampling. The reduction in semantic error rate, from 19% to 11%, is statistically significant at 0.01 level.

We present a series of experiments designed to compare grammar-based and robust approaches to speech understanding, performed in the context of an Open Source medical speech translation system. We used two versions of the system, one grammar-based and one robust, trained off the same training data, and evaluated them on test data collected using both versions of the system. The experiments were constructed so as to avoid several methodological problems which occurred in earlier work reported in the literature. We found that the grammarbased version gave significantly better results than the robust version, with the difference increasing as subjects became more familiar with the system's coverage. The rate of improvement in subject performance was positively affected by providing them with an intelligent online help system.

In this paper, we present evidence that providing users of a speech to speech translation system for emergency diagnosis (MedSLT) with a tool that helps them to learn the coverage greatly improves their success in using the system. In MedSLT, the system uses a grammar-based recogniser that provides more predictable results to the translation component. The help module aims at addressing the lack of robustness inherent in this type of approach. It takes as input the result of a robust statistical recogniser that performs better for out-of-coverage data and produces a list of in-coverage example sentences. These examples are selected from a defined list using a heuristic that prioritises sentences maximising the number of N-grams shared with those extracted from the recognition result.