There is a growing interest in the evaluation of bias, fairness and social impact of Natural Language Processing models and tools. However, little resources are available for this task in languages other than English. Translation of resources originally developed for English is a promising research direction. However, there is also a need for complementing translated resources by newly sourced resources in the original languages and social contexts studied. In order to collect a language resource for the study of biases in Language Models for French, we decided to resort to citizen science. We created three tasks on the LanguageARC citizen science platform to assist with the translation of an existing resource from English into French as well as the collection of complementary resources in native French. We successfully collected data for all three tasks from a total of 102 volunteer participants. Participants from different parts of the world contributed and we noted that although calls sent to mailing lists had a positive impact on participation, some participants pointed barriers to contributions due to the collection platform.

Modern Natural Language Processing relies on the availability of annotated corpora for training and evaluating models. Such resources are scarce, especially for specialized domains in languages other than English. In particular, there are very few resources for semantic similarity in the clinical domain in French. This can be useful for many biomedical natural language processing applications, including text generation. We introduce a definition of similarity that is guided by clinical facts and apply it to the development of a new French corpus of 1,000 sentence pairs manually annotated according to similarity scores. This new sentence similarity corpus is made freely available to the community. We further evaluate the corpus through experiments of automatic similarity measurement. We show that a model of sentence embeddings can capture similarity with state-of-the-art performance on the DEFT STS shared task evaluation data set (Spearman=0.8343). We also show that the corpus is complementary to DEFT STS.

Evaluating bias, fairness, and social impact in monolingual language models is a difficult task. This challenge is further compounded when language modeling occurs in a multilingual context. Considering the implication of evaluation biases for large multilingual language models, we situate the discussion of bias evaluation within a wider context of social scientific research with computational work.We highlight three dimensions of developing multilingual bias evaluation frameworks: (1) increasing transparency through documentation, (2) expanding targets of bias beyond gender, and (3) addressing cultural differences that exist between languages.We further discuss the power dynamics and consequences of training large language models and recommend that researchers remain cognizant of the ramifications of developing such technologies.

Warning: This paper contains explicit statements of offensive stereotypes which may be upsetting.Much work on biases in natural language processing has addressed biases linked to the social and cultural experience of English speaking individuals in the United States. We seek to widen the scope of bias studies by creating material to measure social bias in language models (LMs) against specific demographic groups in France. We build on the US-centered CrowS-pairs dataset to create a multilingual stereotypes dataset that allows for comparability across languages while also characterizing biases that are specific to each country and language. We introduce 1,679 sentence pairs in French that cover stereotypes in ten types of bias like gender and age. 1,467 sentence pairs are translated from CrowS-pairs and 212 are newly crowdsourced. The sentence pairs contrast stereotypes concerning underadvantaged groups with the same sentence concerning advantaged groups. We find that four widely used language models (three French, one multilingual) favor sentences that express stereotypes in most bias categories. We report on the translation process from English into French, which led to a characterization of stereotypes in CrowS-pairs including the identification of US-centric cultural traits. We offer guidelines to further extend the dataset to other languages and cultural environments.

Le TAL repose sur la disponibilité de corpus annotés pour l’entraînement et l’évaluation de modèles. Il existe très peu de ressources pour la similarité sémantique dans le domaine clinique en français. Dans cette étude, nous proposons une définition de la similarité guidée par l’analyse clinique et l’appliquons au développement d’un nouveau corpus partagé de 1 000 paires de phrases annotées manuellement en scores de similarité. Nous évaluons ensuite le corpus par des expériences de mesure automatique de similarité. Nous montrons ainsi qu’un modèle de plongements de phrases peut capturer la similarité avec des performances à l’état de l’art sur le corpus DEFT STS (Spearman=0,8343). Nous montrons également que le contenu du corpus CLISTER est complémentaire de celui de DEFT STS.

Afin de permettre l’étude des biais en traitement automatique de la langue au delà de l’anglais américain, nous enrichissons le corpus américain CrowS-pairs de 1 677 paires de phrases en français représentant des stéréotypes portant sur dix catégories telles que le genre. 1 467 paires de phrases sont traduites à partir de CrowS-pairs et 210 sont nouvellement recueillies puis traduites en anglais. Selon le principe des paires minimales, les phrases du corpus contrastent un énoncé stéréotypé concernant un groupe défavorisé et son équivalent pour un groupe favorisé. Nous montrons que quatre modèles de langue favorisent les énoncés qui expriment des stéréotypes dans la plupart des catégories. Nous décrivons le processus de traduction et formulons des recommandations pour étendre le corpus à d’autres langues. Attention : Cet article contient des énoncés de stéréotypes qui peuvent être choquants.

The reviewing procedure has been identified as one of the major issues in the current situation of the NLP field. While it is implicitly assumed that junior researcher learn reviewing during their PhD project, this might not always be the case. Additionally, with the growing NLP community and the efforts in the context of widening the NLP community, researchers joining the field might not have the opportunity to practise reviewing. This tutorial fills in this gap by providing an opportunity to learn the basics of reviewing. Also more experienced researchers might find this tutorial interesting to revise their reviewing procedure.

Modern Natural Language Processing (NLP) makes intensive use of deep learning methods because of the accuracy they offer for a variety of applications. Due to the significant environmental impact of deep learning, cost-benefit analysis including carbon footprint as well as accuracy measures has been suggested to better document the use of NLP methods for research or deployment. In this paper, we review the tools that are available to measure energy use and CO2 emissions of NLP methods. We describe the scope of the measures provided and compare the use of six tools (carbon tracker, experiment impact tracker, green algorithms, ML CO2 impact, energy usage and cumulator) on named entity recognition experiments performed on different computational set-ups (local server vs. computing facility). Based on these findings, we propose actionable recommendations to accurately measure the environmental impact of NLP experiments.

In the sixth edition of the WMT Biomedical Task, we addressed a total of eight language pairs, namely English/German, English/French, English/Spanish, English/Portuguese, English/Chinese, English/Russian, English/Italian, and English/Basque. Further, our tests were composed of three types of textual test sets. New to this year, we released a test set of summaries of animal experiments, in addition to the test sets of scientific abstracts and terminologies. We received a total of 107 submissions from 15 teams from 6 countries.

Machine translation of scientific abstracts and terminologies has the potential to support health professionals and biomedical researchers in some of their activities. In the fifth edition of the WMT Biomedical Task, we addressed a total of eight language pairs. Five language pairs were previously addressed in past editions of the shared task, namely, English/German, English/French, English/Spanish, English/Portuguese, and English/Chinese. Three additional languages pairs were also introduced this year: English/Russian, English/Italian, and English/Basque. The task addressed the evaluation of both scientific abstracts (all language pairs) and terminologies (English/Basque only). We received submissions from a total of 20 teams. For recurring language pairs, we observed an improvement in the translations in terms of automatic scores and qualitative evaluations, compared to previous years.

This tutorial will cover the theory and practice of reviewing research in natural language processing. Heavy reviewing burdens on natural language processing researchers have made it clear that our community needs to increase the size of our pool of potential reviewers. Simultaneously, notable “false negatives”---rejection by our conferences of work that was later shown to be tremendously important after acceptance by other conferences—have raised awareness of the fact that our reviewing practices leave something to be desired. We do not often talk about “false positives” with respect to conference papers, but leaders in the field have noted that we seem to have a publication bias towards papers that report high performance, with perhaps not much else of interest in them. It need not be this way. Reviewing is a learnable skill, and you will learn it here via lectures and a considerable amount of hands-on practice.

Background: Parallel corpora are used to train and evaluate machine translation systems. To alleviate the cost of producing parallel resources for evaluation campaigns, existing corpora are leveraged. However, little information may be available about the methods used for producing the corpus, including translation direction. Objective: To gain insight on MEDLINE parallel corpus used in the biomedical task at the Workshop on Machine Translation in 2019 (WMT 2019). Material and Methods: Contact information for the authors of MEDLINE articles included in the English/Spanish (EN/ES), English/French (EN/FR), and English/Portuguese (EN/PT) WMT 2019 test sets was obtained from PubMed and publisher websites. The authors were asked about their abstract writing practices in a survey. Results: The response rate was above 20%. Authors reported that they are mainly native speakers of languages other than English. Although manual translation, sometimes via professional translation services, was commonly used for abstract translation, authors of articles in the EN/ES and EN/PT sets also relied on post-edited machine translation. Discussion: This study provides a characterization of MEDLINE authors’ language skills and abstract writing practices. Conclusion: The information collected in this study will be used to inform test set design for the next WMT biomedical task.

La résolution de la coréférence est un élément essentiel pour la constitution automatique de chronologies médicales à partir des dossiers médicaux électroniques. Dans ce travail, nous présentons une approche neuronale pour la résolution de la coréférence dans des textes médicaux écrits en anglais pour les entités générales et cliniques en nous évaluant dans le cadre de référence pour cette tâche que constitue la tâche 1C de la campagne i2b2 2011.

With recent efforts in drawing attention to the task of replicating and/or reproducing results, for example in the context of COLING 2018 and various LREC workshops, the question arises how the NLP community views the topic of replicability in general. Using a survey, in which we involve members of the NLP community, we investigate how our community perceives this topic, its relevance and options for improvement. Based on over two hundred participants, the survey results confirm earlier observations, that successful reproducibility requires more than having access to code and data. Additionally, the results show that the topic has to be tackled from the authors’, reviewers’ and community’s side.

Systematic reviews are important in evidence based medicine, but are expensive to produce. Automating or semi-automating the data extraction of index test, target condition, and reference standard from articles has the potential to decrease the cost of conducting systematic reviews of diagnostic test accuracy, but relevant training data is not available. We create a distantly supervised dataset of approximately 90,000 sentences, and let two experts manually annotate a small subset of around 1,000 sentences for evaluation. We evaluate the performance of BioBERT and logistic regression for ranking the sentences, and compare the performance for distant and direct supervision. Our results suggest that distant supervision can work as well as, or better than direct supervision on this problem, and that distantly trained models can perform as well as, or better than human annotators.

In the fourth edition of the WMT Biomedical Translation task, we considered a total of six languages, namely Chinese (zh), English (en), French (fr), German (de), Portuguese (pt), and Spanish (es). We performed an evaluation of automatic translations for a total of 10 language directions, namely, zh/en, en/zh, fr/en, en/fr, de/en, en/de, pt/en, en/pt, es/en, and en/es. We provided training data based on MEDLINE abstracts for eight of the 10 language pairs and test sets for all of them. In addition to that, we offered a new sub-task for the translation of terms in biomedical terminologies for the en/es language direction. Higher BLEU scores (close to 0.5) were obtained for the es/en, en/es and en/pt test sets, as well as for the terminology sub-task. After manual validation of the primary runs, some submissions were judged to be better than the reference translations, for instance, for de/en, en/es and es/en.

La détection de frontières de phrase est généralement considéré comme un problème résolu. Cependant, les outils performant sur des textes en domaine général, ne le sont pas forcement sur des domaines spécialisés, ce qui peut engendrer des dégradations de performance des outils intervenant en aval dans une chaîne de traitement automatique s’appuyant sur des textes découpés en phrases. Dans cet article, nous évaluons 5 outils de segmentation en phrase sur 3 corpus issus de différent domaines. Nous ré-entrainerons l’un de ces outils sur un corpus de spécialité pour étudier l’adaptation en domaine. Notamment, nous utilisons un nouveau corpus biomédical annoté spécifiquement pour cette tâche. La detection de frontières de phrase à l’aide d’un modèle OpenNLP entraîné sur un corpus clinique offre une F-mesure de .73, contre .66 pour la version standard de l’outil.

Many applications in biomedical natural language processing rely on sequence tagging as an initial step to perform more complex analysis. To support text analysis in the biomedical domain, we introduce Yet Another SEquence Tagger (YASET), an open-source multi purpose sequence tagger that implements state-of-the-art deep learning algorithms for sequence tagging. Herein, we evaluate YASET on part-of-speech tagging and named entity recognition in a variety of text genres including articles from the biomedical literature in English and clinical narratives in French. To further characterize performance, we report distributions over 30 runs and different sizes of training datasets. YASET provides state-of-the-art performance on the CoNLL 2003 NER dataset (F1=0.87), MEDPOST corpus (F1=0.97), MERLoT corpus (F1=0.99) and NCBI disease corpus (F1=0.81). We believe that YASET is a versatile and efficient tool that can be used for sequence tagging in biomedical and clinical texts.

Machine translation enables the automatic translation of textual documents between languages and can facilitate access to information only available in a given language for non-speakers of this language, e.g. research results presented in scientific publications. In this paper, we provide an overview of the Biomedical Translation shared task in the Workshop on Machine Translation (WMT) 2018, which specifically examined the performance of machine translation systems for biomedical texts. This year, we provided test sets of scientific publications from two sources (EDP and Medline) and for six language pairs (English with each of Chinese, French, German, Portuguese, Romanian and Spanish). We describe the development of the various test sets, the submissions that we received and the evaluations that we carried out. We obtained a total of 39 runs from six teams and some of this year’s BLEU scores were somewhat higher that last year’s, especially for teams that made use of biomedical resources or state-of-the-art MT algorithms (e.g. Transformer). Finally, our manual evaluation scored automatic translations higher than the reference translations for German and Spanish.

In this paper we present our participation to SemEval 2017 Task 12. We used a neural network based approach for entity and temporal relation extraction, and experimented with two domain adaptation strategies. We achieved competitive performance for both tasks.

Les revues systématiques de la littérature dans le domaine biomédical reposent essentiellement sur le travail bibliographique manuel d’experts. Nous évaluons les performances de la classification supervisée pour la découverte automatique d’articles à l’aide de plusieurs définitions des critères d’inclusion. Nous appliquons un modèle de regression logistique sur deux corpus issus de revues systématiques conduites dans le domaine du traitement automatique de la langue et de l’efficacité des médicaments. La classification offre une aire sous la courbe moyenne (AUC) de 0.769 si le classifieur est contruit à partir des jugements experts portés sur les titres et résumés des articles, et de 0.835 si on utilise les jugements portés sur le texte intégral. Ces résultats indiquent l’importance des jugements portés dès le début du processus de sélection pour développer un classifieur efficace pour accélérer l’élaboration des revues systématiques à l’aide d’un algorithme de classification standard.

Nous proposons des démonstrations de trois outils développés par le LIMSI en traitement automatique des langues appliqué au domaine biomédical : la détection de concepts médicaux dans des textes courts, la catégorisation d’articles scientifiques pour l’assistance à l’écriture de revues systématiques, et l’anonymisation de textes cliniques.

We present a neural architecture for containment relation identification between medical events and/or temporal expressions. We experiment on a corpus of de-identified clinical notes in English from the Mayo Clinic, namely the THYME corpus. Our model achieves an F-measure of 0.613 and outperforms the best result reported on this corpus to date.

In this paper, we present a method for temporal relation extraction from clinical narratives in French and in English. We experiment on two comparable corpora, the MERLOT corpus and the THYME corpus, and show that a common approach can be used for both languages.

Very few datasets have been released for the evaluation of diagnosis coding with the International Classification of Diseases, and only one so far in a language other than English. This paper describes a large-scale dataset prepared from French death certificates, and the problems which needed to be solved to turn it into a dataset suitable for the application of machine learning and natural language processing methods of ICD-10 coding. The dataset includes the free-text statements written by medical doctors, the associated meta-data, the human coder-assigned codes for each statement, as well as the statement segments which supported the coder’s decision for each code. The dataset comprises 93,694 death certificates totalling 276,103 statements and 377,677 ICD-10 code assignments (3,457 unique codes). It was made available for an international automated coding shared task, which attracted five participating teams. An extended version of the dataset will be used in a new edition of the shared task.

Electronic Health Records (EHRs) are increasingly available in modern health care institutions either through the direct creation of electronic documents in hospitals’ health information systems, or through the digitization of historical paper records. Each EHR creation method yields the need for sophisticated text reuse detection tools in order to prepare the EHR collections for efficient secondary use relying on Natural Language Processing methods. Herein, we address the detection of two types of text reuse in French EHRs: 1) the detection of updated versions of the same document and 2) the detection of document duplicates that still bear surface differences due to OCR or de-identification processing. We present a robust text reuse detection method to automatically identify redundant document pairs in two French EHR corpora that achieves an overall macro F-measure of 0.68 and 0.60, respectively and correctly identifies all redundant document pairs of interest.

The biomedical scientific literature is a rich source of information not only in the English language, for which it is more abundant, but also in other languages, such as Portuguese, Spanish and French. We present the first freely available parallel corpus of scientific publications for the biomedical domain. Documents from the ”Biological Sciences” and ”Health Sciences” categories were retrieved from the Scielo database and parallel titles and abstracts are available for the following language pairs: Portuguese/English (about 86,000 documents in total), Spanish/English (about 95,000 documents) and French/English (about 2,000 documents). Additionally, monolingual data was also collected for all four languages. Sentences in the parallel corpus were automatically aligned and a manual analysis of 200 documents by native experts found that a minimum of 79% of sentences were correctly aligned in all language pairs. We demonstrate the utility of the corpus by running baseline machine translation experiments. We show that for all language pairs, a statistical machine translation system trained on the parallel corpora achieves performance that rivals or exceeds the state of the art in the biomedical domain. Furthermore, the corpora are currently being used in the biomedical task in the First Conference on Machine Translation (WMT’16).

L’analyse temporelle des documents cliniques permet d’obtenir des représentations riches des informations contenues dans les dossiers électroniques patient. Cette analyse repose sur l’extraction d’événements, d’expressions temporelles et des relations entre eux. Dans ce travail, nous considérons que nous disposons des événements et des expressions temporelles pertinents et nous nous intéressons aux relations temporelles entre deux événements ou entre un événement et une expression temporelle. Nous présentons des modèles de classification supervisée pour l’extraction de des relations en français et en anglais. Les performances obtenues sont comparables dans les deux langues, suggérant ainsi que différents domaines cliniques et différentes langues pourraient être abordés de manière similaire.

Les références à des phénomènes du monde réel et à leur caractérisation temporelle se retrouvent dans beaucoup de types de discours en langue naturelle. Ainsi, l’analyse temporelle apparaît comme un élément important en traitement automatique de la langue. Cet article présente une analyse de textes en domaine de spécialité du point de vue temporel. En s’appuyant sur un corpus de documents issus de plusieurs dossiers électroniques patient désidentifiés, nous décrivons la construction d’une ressource annotée en expressions temporelles selon la norme TimeML. Par suite, nous utilisons cette ressource pour évaluer plusieurs méthodes d’extraction automatique d’expressions temporelles adaptées au domaine médical. Notre meilleur système statistique offre une performance de 0,91 de F-mesure, surpassant pour l’identification le système état de l’art HeidelTime. La comparaison de notre corpus de travail avec le corpus journalistique FR-Timebank permet également de caractériser les différences d’utilisation des expressions temporelles dans deux domaines de spécialité.

L’étiquetage morpho-syntaxique est une tâche fondamentale du Traitement Automatique de la Langue, sur laquelle reposent souvent des traitements plus complexes tels que l’extraction d’information ou la traduction automatique. L’étiquetage en domaine de spécialité est limité par la disponibilité d’outils et de corpus annotés spécifiques au domaine. Dans cet article, nous présentons le développement d’un corpus clinique du français annoté morpho-syntaxiquement à l’aide d’un jeu d’étiquettes issus des guides d’annotation French Treebank et Multitag. L’analyse de ce corpus nous permet de caractériser le domaine clinique et de dégager les points clés pour l’adaptation d’outils d’analyse morpho-syntaxique à ce domaine. Nous montrons également les limites d’un outil entraîné sur un corpus journalistique appliqué au domaine clinique. En perspective de ce travail, nous envisageons une application du corpus clinique annoté pour améliorer l’étiquetage morpho-syntaxique des documents cliniques en français.

Annotated corpora are essential resources for many applications in Natural Language Processing. They provide insight on the linguistic and semantic characteristics of the genre and domain covered, and can be used for the training and evaluation of automatic tools. In the biomedical domain, annotated corpora of English texts have become available for several genres and subfields. However, very few similar resources are available for languages other than English. In this paper we present an effort to produce a high-quality corpus of clinical documents in French, annotated with a comprehensive scheme of entities and relations. We present the annotation scheme as well as the results of a pilot annotation study covering 35 clinical documents in a variety of subfields and genres. We show that high inter-annotator agreement can be achieved using a complex annotation scheme.

The biomedical domain offers a wealth of linguistic resources for Natural Language Processing, including terminologies and corpora. While many of these resources are prominently available for English, other languages including French benefit from substantial coverage thanks to the contribution of an active community over the past decades. However, access to terminological resources in languages other than English may not be as straight-forward as access to their English counterparts. Herein, we review the extent of resource coverage for French and give pointers to access French-language resources. We also discuss the sources and methods for making additional material available for French.

L’indexation est une composante importante de tout système de recherche d’information. Dans MEDLINE, la base documentaire de référence pour la littérature du domaine biomédical, le contenu des articles référencés est indexé à l’aide de descripteurs issus du thésaurus MeSH. Avec l’augmentation constante de publications à indexer pour maintenir la base à jour, le besoin d’outils automatiques se fait pressant pour les indexeurs. Dans cet article, nous décrivons l’utilisation et l’adaptation de la Programmation Logique Inductive (PLI) pour découvrir des règles d’indexation permettant de générer automatiquement des recommandations d’indexation pour MEDLINE. Les résultats obtenus par cette approche originale sont très satisfaisants comparés à ceux obtenus à l’aide de règles manuelles lorsque celles-ci existent. Ainsi, les jeux de règles obtenus par PLI devraient être prochainement intégrés au système produisant les recommandations d’indexation automatique pour MEDLINE.

Depuis quelques années, médecins et documentalistes doivent faire face à une demande croissante dans le domaine du codage médico-économique et de l’indexation des diverses sources d’information disponibles dans le domaine de la santé. Il est donc nécessaire de développer des outils d’indexation automatique qui réduisent les délais d’indexation et facilitent l’accès aux ressources médicales. Nous proposons deux méthodes d’indexation automatique de ressources de santé à l’aide de paires de descripteurs MeSH. La combinaison de ces deux méthodes permet d’optimiser les résulats en exploitant la complémentarité des approches. Les performances obtenues sont équivalentes à celles des outils de la littérature pour une indexation à l’aide de descripteurs seuls.

Nous présentons ici le système d’indexation automatique actuellement en cours de développement dans l’équipe CISMeF afin d’aider les documentalistes lors de l’indexation de ressources de santé. Nous détaillons l’architecture du système pour l’extraction de mots clés MeSH, et présentons les résultats d’une première évaluation. La stratégie d’indexation choisie atteint une précision comparable à celle des systèmes existants. De plus, elle permet d’extraire des paires mot clé/qualificatif, et non des termes isolés, ce qui constitue une indexation beaucoup plus fine. Les travaux en cours s’attachent à étendre la couverture des dictionnaires, et des tests à plus grande échelle sont envisagés afin de valider le système et d’évaluer sa valeur ajoutée dans le travail quotidien des documentalistes.