The automatic translation of spoken language into pictogram units can facilitate communication involving individuals with language impairments. However, there is no established translation formalism or publicly available datasets for training end-to-end speech translation systems. This paper introduces the first aligned speech, text, and pictogram translation dataset ever created in any language. We provide a French dataset that contains 230 hours of speech resources. We create a rule-based pictogram grammar with a restricted vocabulary and include a discussion of the strategic decisions involved. It takes advantage of an in-depth linguistic study of resources taken from the ARASAAC website. We validate these rules through multiple post-editing phases by expert annotators. The constructed dataset is then used to experiment with a Speech-to-Pictogram cascade model, which employs state-of-the-art Automatic Speech Recognition models. The dataset is freely available under a non-commercial licence. This marks a starting point to conduct research into the automatic translation of speech into pictogram units.

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.

Neural text generation is receiving broad attention with the publication of new tools such as ChatGPT. The main reason for that is that the achieved quality of the generated text may be attributed to a human writer by the naked eye of a human evaluator. In this paper, we propose a new corpus in French and English for the task of recognising automatically generated texts and we conduct a study of how humans perceive the text. Our results show, as previous work before the ChatGPT era, that the generated texts by tools such as ChatGPT share some common characteristics but they are not clearly identifiable which generates different perceptions of these texts.

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.

Nous présentons un ensemble de trois interfaces web pour la création de données en pictogrammes dans le cadre du projet ANR Propicto. Chacune a un objectif précis : annoter des données textuelles en pictogrammes ARASAAC, créer un vocabulaire en pictogrammes, et post-éditer des phrases annotées en pictogrammes. Bien que nécessaire pour des outils de traduction automatique vers les unités pictographiques, actuellement, presque aucune ressource annotée n’existe. Cet article présente les spécificités de ces plateformes web (disponibles en ligne gratuitement) et leur utilité.

Nous présentons Voice2Picto, un système de traduction permettant, à partir de l’oral, de proposer une séquence de pictogrammes correspondants. S’appuyant sur des technologies du traitement automatique du langage naturel, l’outil a deux objectifs : améliorer l’accès à la communication pour (1) les personnes allophones dans un contexte d’urgence médicale, et (2) pour les personnes avec des difficultés de parole. Il permettra aux personnes des services hospitaliers, et aux familles de véhiculer un message en pictogrammes facilement compréhensible auprès de personnes ne pouvant communiquer via les canaux traditionnels de communication (parole, gestes, langue des signes). Dans cet article, nous décrivons l’architecture du système de Voice2Picto et les pistes futures. L’application est en open-source via un dépôt Git : https://github.com/macairececile/Voice2Picto.

This paper describes the MAKE-NMTVIZ Systems trained for the WMT 2023 Literary task. As a primary submission, we used Train, Valid1, test1 as part of the GuoFeng corpus (Wang et al., 2023) to fine-tune the mBART50 model with Chinese-English data. We followed very similar training parameters to (Lee et al. 2022) when fine-tuning mBART50. We trained for 3 epochs, using gelu as an activation function, with a learning rate of 0.05, dropout of 0.1 and a batch size of 16. We decoded using a beam search of size 5. For our contrastive1 submission, we implemented a fine-tuned concatenation transformer (Lupo et al., 2023). The training was developed in two steps: (i) a sentence-level transformer was implemented for 10 epochs trained using general, test1, and valid1 data (more details in contrastive2 system); (ii) second, we fine-tuned at document-level using 3-sentence concatenation for 4 epochs using train, test2, and valid2 data. During the fine-tuning, we used ReLU as an activation function, with an inverse square root learning rate, dropout of 0.1, and a batch size of 64. We decoded using a beam search of size. Four our contrastive2 and last submission, we implemented a sentence-level transformer model (Vaswani et al., 2017). The model was trained with general data for 10 epochs using general-purpose, test1, and valid 1 data. The training parameters were an inverse square root scheduled learning rate, a dropout of 0.1, and a batch size of 64. We decoded using a beam search of size 4. We then compared the three translation outputs from an interdisciplinary perspective, investigating some of the effects of sentence- vs document-based training. Computer scientists, translators and corpus linguists discussed the linguistic remaining issues for this discourse-level literary translation.

Cette démonstration présente les avancées d’ACCOLÉ (Annotation Collaborative d’erreurs de traduction pour COrpus aLignÉs), qui en plus de proposer une gestion simplifiée des corpus et des typologies d’erreurs, l’annotation d’erreurs pour des corpus de traduction bilingues alignés, la collaboration et/ou supervision lors de l’annotation, la recherche de modèle d’erreurs dans les annotations, permet désormais d’annoter les Expressions Polylexicales (EPL) dans des textes monolingues en français, et d’accéder à l’annotation d’erreurs pour des corpus de traduction multicibles. Dans cet article, après un bref rappel des fonctionnalités d’ACCOLÉ, nous explicitons les fonctionnalités de chaque nouveauté.

This article presents a resource that links WordNet, the widely known lexical and semantic database, and Arasaac, the largest freely available database of pictograms. Pictograms are a tool that is more and more used by people with cognitive or communication disabilities. However, they are mainly used manually via workbooks, whereas caregivers and families would like to use more automated tools (use speech to generate pictograms, for example). In order to make it possible to use pictograms automatically in NLP applications, we propose a database that links them to semantic knowledge. This resource is particularly interesting for the creation of applications that help people with cognitive disabilities, such as text-to-picto, speech-to-picto, picto-to-speech... In this article, we explain the needs for this database and the problems that have been identified. Currently, this resource combines approximately 800 pictograms with their corresponding WordNet synsets and it is accessible both through a digital collection and via an SQL database. Finally, we propose a method with associated tools to make our resource language-independent: this method was applied to create a first text-to-picto prototype for the French language. Our resource is distributed freely under a Creative Commons license at the following URL: https://github.com/getalp/Arasaac-WN.

We conduct in this work an evaluation study comparing offline and online neural machine translation architectures. Two sequence-to-sequence models: convolutional Pervasive Attention (Elbayad et al. 2018) and attention-based Transformer (Vaswani et al. 2017) are considered. We investigate, for both architectures, the impact of online decoding constraints on the translation quality through a carefully designed human evaluation on English-German and German-English language pairs, the latter being particularly sensitive to latency constraints. The evaluation results allow us to identify the strengths and shortcomings of each model when we shift to the online setup.

La plateforme ACCOLÉ (Annotation Collaborative d’erreurs de traduction pour COrpus aLignÉs) propose une palette de services innovants permettant de répondre aux besoins modernes d’analyse d’erreurs de traduction : gestion simplifiée des corpus et des typologies d’erreurs, annotation d’erreurs efficace, collaboration et/ou supervision lors de l’annotation, recherche de modèle d’erreurs dans les annotations.

Corpus-based approaches to machine translation (MT) rely on the availability of parallel corpora. To produce user-acceptable translation outputs, such systems need high quality data to be efficiency trained, optimized and evaluated. However, building high quality dataset is a relatively expensive task. In this paper, we describe the data collection and analysis of a large database of 10.881 SMT translation output hypotheses manually corrected. These post-editions were collected using Amazon's Mechanical Turk, following some ethical guidelines. A complete analysis of the collected data pointed out a high quality of the corrections with more than 87 % of the collected post-editions that improve hypotheses and more than 94 % of the crowdsourced post-editions which are at least of professional quality. We also post-edited 1,500 gold-standard reference translations (of bilingual parallel corpora generated by professional) and noticed that 72 % of these translations needed to be corrected during post-edition. We computed a proximity measure between the differents kind of translations and pointed out that reference translations are as far from the hypotheses than from the corrected hypotheses (i.e. the post-editions). In light of these last findings, we discuss the adequation of text-based generated reference translations to train setence-to-sentence based SMT systems.