Elena V. Epure

Also published as: Elena V. Epure


Probing Pre-trained Auto-regressive Language Models for Named Entity Typing and Recognition
Elena V. Epure | Romain Hennequin
Proceedings of the Thirteenth Language Resources and Evaluation Conference

Multiple works have proposed to probe language models (LMs) for generalization in named entity (NE) typing (NET) and recognition (NER). However, little has been done in this direction for auto-regressive models despite their popularity and potential to express a wide variety of NLP tasks in the same unified format. We propose a new methodology to probe auto-regressive LMs for NET and NER generalization, which draws inspiration from human linguistic behavior, by resorting to meta-learning. We study NEs of various types individually by designing a zero-shot transfer strategy for NET. Then, we probe the model for NER by providing a few examples at inference. We introduce a novel procedure to assess the model’s memorization of NEs and report the memorization’s impact on the results. Our findings show that: 1) GPT2, a common pre-trained auto-regressive LM, without any fine-tuning for NET or NER, performs the tasksfairly well; 2) name irregularity when common for a NE type could be an effective exploitable cue; 3) the model seems to rely more on NE than contextual cues in few-shot NER; 4) NEs with words absent during LM pre-training are very challenging for both NET and NER.


Are Metal Fans Angrier than Jazz Fans? A Genre-Wise Exploration of the Emotional Language of Music Listeners on Reddit
Vipul Mishra | Kongmeng Liew | Elena V. Epure | Romain Hennequin | Eiji Aramaki
Proceedings of the 2nd Workshop on NLP for Music and Spoken Audio (NLP4MusA)

Modéliser la perception des genres musicaux à travers différentes cultures à partir de ressources linguistiques (Modeling the Music Genre Perception across Language-Bound Cultures )
Elena V. Epure | Guillaume Salha-Galvan | Manuel Moussallam | Romain Hennequin
Actes de la 28e Conférence sur le Traitement Automatique des Langues Naturelles. Volume 1 : conférence principale

Nous résumons nos travaux de recherche, présentés à la conférence EMNLP 2020 et portant sur la modélisation de la perception des genres musicaux à travers différentes cultures, à partir de représentations sémantiques spécifiques à différentes langues.


Muzeeglot : annotation multilingue et multi-sources d’entités musicales à partir de représentations de genres musicaux (Muzeeglot : cross-lingual multi-source music item annotation from music genre embeddings)
Elena V. Epure | Guillaume Salha | Félix Voituret | Marion Baranes | Romain Hennequin
Actes de la 6e conférence conjointe Journées d'Études sur la Parole (JEP, 33e édition), Traitement Automatique des Langues Naturelles (TALN, 27e édition), Rencontre des Étudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (RÉCITAL, 22e édition). Volume 4 : Démonstrations et résumés d'articles internationaux

Au sein de cette démonstration, nous présentons Muzeeglot, une interface web permettant de visualiser des espaces de représentations de genres musicaux provenant de sources variées et de langues différentes. Nous montrons l’efficacité de notre système à prédire automatiquement les genres correspondant à une entité musicale (titre, artiste, album...) selon une certaine source ou langue, étant données des annotations provenant de sources ou de langues différentes.

Modeling the Music Genre Perception across Language-Bound Cultures
Elena V. Epure | Guillaume Salha | Manuel Moussallam | Romain Hennequin
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

The music genre perception expressed through human annotations of artists or albums varies significantly across language-bound cultures. These variations cannot be modeled as mere translations since we also need to account for cultural differences in the music genre perception. In this work, we study the feasibility of obtaining relevant cross-lingual, culture-specific music genre annotations based only on language-specific semantic representations, namely distributed concept embeddings and ontologies. Our study, focused on six languages, shows that unsupervised cross-lingual music genre annotation is feasible with high accuracy, especially when combining both types of representations. This approach of studying music genres is the most extensive to date and has many implications in musicology and music information retrieval. Besides, we introduce a new, domain-dependent cross-lingual corpus to benchmark state of the art multilingual pre-trained embedding models.