Elena Epure


2024

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Distinguishing Fictional Voices: a Study of Authorship Verification Models for Quotation Attribution
Gaspard Michel | Elena Epure | Romain Hennequin | Christophe Cerisara
Proceedings of the 8th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (LaTeCH-CLfL 2024)

Recent approaches to automatically detect the speaker of an utterance of direct speech often disregard general information about characters in favor of local information found in the context, such as surrounding mentions of entities. In this work, we explore stylistic representations of characters built by encoding their quotes with off-the-shelf pretrained Authorship Verification models in a large corpus of English novels (the Project Dialogism Novel Corpus). Results suggest that the combination of stylistic and topical information captured in some of these models accurately distinguish characters among each other, but does not necessarily improve over semantic-only models when attributing quotes. However, these results vary across novels and more investigation of stylometric models particularly tailored for literary texts and the study of characters should be conducted.

2023

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A Human Subject Study of Named Entity Recognition in Conversational Music Recommendation Queries
Elena Epure | Romain Hennequin
Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics

We conducted a human subject study of named entity recognition on a noisy corpus of conversational music recommendation queries, with many irregular and novel named entities. We evaluated the human NER linguistic behaviour in these challenging conditions and compared it with the most common NER systems nowadays, fine-tuned transformers. Our goal was to learn about the task to guide the design of better evaluation methods and NER algorithms. The results showed that NER in our context was quite hard for both human and algorithms under a strict evaluation schema; humans had higher precision, while the model higher recall because of entity exposure especially during pre-training; and entity types had different error patterns (e.g. frequent typing errors for artists). The released corpus goes beyond predefined frames of interaction and can support future work in conversational music recommendation.

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Automatic Annotation of Direct Speech in Written French Narratives
Noé Durandard | Viet Anh Tran | Gaspard Michel | Elena Epure
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

The automatic annotation of direct speech (AADS) in written text has been often used in computational narrative understanding. Methods based on either rules or deep neural networks have been explored, in particular for English or German languages. Yet, for French, our target language, not many works exist. Our goal is to create a unified framework to design and evaluate AADS models in French. For this, we consolidated the largest-to-date French narrative dataset annotated with DS per word; we adapted various baselines for sequence labelling or from AADS in other languages; and we designed and conducted an extensive evaluation focused on generalisation. Results show that the task still requires substantial efforts and emphasise characteristics of each baseline. Although this framework could be improved, it is a step further to encourage more research on the topic.

2022

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Data-Efficient Playlist Captioning With Musical and Linguistic Knowledge
Giovanni Gabbolini | Romain Hennequin | Elena Epure
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Music streaming services feature billions of playlists created by users, professional editors or algorithms. In this content overload scenario, it is crucial to characterise playlists, so that music can be effectively organised and accessed. Playlist titles and descriptions are proposed in natural language either manually by music editors and users or automatically from pre-defined templates. However, the former is time-consuming while the latter is limited by the vocabulary and covered music themes. In this work, we propose PlayNTell, a data-efficient multi-modal encoder-decoder model for automatic playlist captioning. Compared to existing music captioning algorithms, PlayNTell leverages also linguistic and musical knowledge to generate correct and thematic captions. We benchmark PlayNTell on a new editorial playlists dataset collected from two major music streaming services. PlayNTell yields 2x-3x higher BLEU@4 and CIDEr than state of the art captioning algorithms.

2021

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Proceedings of the 2nd Workshop on NLP for Music and Spoken Audio (NLP4MusA)
Sergio Oramas | Elena Epure | Luis Espinosa-Anke | Rosie Jones | Massimo Quadrana | Mohamed Sordo | Kento Watanabe
Proceedings of the 2nd Workshop on NLP for Music and Spoken Audio (NLP4MusA)

2020

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Proceedings of the 1st Workshop on NLP for Music and Audio (NLP4MusA)
Sergio Oramas | Luis Espinosa-Anke | Elena Epure | Rosie Jones | Mohamed Sordo | Massimo Quadrana | Kento Watanabe
Proceedings of the 1st Workshop on NLP for Music and Audio (NLP4MusA)

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Prediction of user listening contexts for music playlists
Jeong Choi | Anis Khlif | Elena Epure
Proceedings of the 1st Workshop on NLP for Music and Audio (NLP4MusA)