Fanny Ducel


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The Elephant in the Room: Analyzing the Presence of Big Tech in Natural Language Processing Research
Mohamed Abdalla | Jan Philip Wahle | Terry Ruas | Aurélie Névéol | Fanny Ducel | Saif Mohammad | Karen Fort
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Recent advances in deep learning methods for natural language processing (NLP) have created new business opportunities and made NLP research critical for industry development. As one of the big players in the field of NLP, together with governments and universities, it is important to track the influence of industry on research. In this study, we seek to quantify and characterize industry presence in the NLP community over time. Using a corpus with comprehensive metadata of 78,187 NLP publications and 701 resumes of NLP publication authors, we explore the industry presence in the field since the early 90s. We find that industry presence among NLP authors has been steady before a steep increase over the past five years (180% growth from 2017 to 2022). A few companies account for most of the publications and provide funding to academic researchers through grants and internships. Our study shows that the presence and impact of the industry on natural language processing research are significant and fast-growing. This work calls for increased transparency of industry influence in the field.


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Do we Name the Languages we Study? The #BenderRule in LREC and ACL articles
Fanny Ducel | Karën Fort | Gaël Lejeune | Yves Lepage
Proceedings of the Thirteenth Language Resources and Evaluation Conference

This article studies the application of the #BenderRule in Natural Language Processing (NLP) articles according to two dimensions. Firstly, in a contrastive manner, by considering two major international conferences, LREC and ACL, and secondly, in a diachronic manner, by inspecting nearly 14,000 articles over a period of time ranging from 2000 to 2020 for LREC and from 1979 to 2020 for ACL. For this purpose, we created a corpus from LREC and ACL articles from the above-mentioned periods, from which we manually annotated nearly 1,000. We then developed two classifiers to automatically annotate the rest of the corpus. Our results show that LREC articles tend to respect the #BenderRule (80 to 90% of them respect it), whereas 30 to 40% of ACL articles do not. Interestingly, over the considered periods, the results appear to be stable for the two conferences, even though a rebound in ACL 2020 could be a sign of the influence of the blog post about the #BenderRule.

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Langues par défaut? Analyse contrastive et diachronique des langues non citées dans les articles de TALN et d’ACL (Contrastive and diachronic study of unmentioned (by default ?) languages in TALN and ACL We study the application of the #BenderRule in natural language processing articles, taking into account a contrastive and a diachronic dimensions, by examining the proceedings of two NLP conferences, TALN and ACL, over time)
Fanny Ducel | Karën Fort | Gaël Lejeune | Yves Lepage
Actes de la 29e Conférence sur le Traitement Automatique des Langues Naturelles. Volume 1 : conférence principale

Cet article étudie l’application de la #RègledeBender dans des articles de traitement automatique des langues (TAL), en prenant en compte une dimension contrastive, par l’examen des actes de deux conférences du domaine, TALN et ACL, et une dimension diachronique, en examinant ces conférences au fil du temps. Un échantillon d’articles a été annoté manuellement et deux classifieurs ont été développés afin d’annoter automatiquement les autres articles. Nous quantifions ainsi l’application de la #RègledeBender, et mettons en évidence un léger mieux en faveur de TALN sur cet aspect.