Papers by Romain Hennequin
Improving Quotation Attribution with Fictional Character Embeddings (2024.findings-emnlp)
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| Challenge: | Recent methods to attribute quotes to human logic lack character representations, which often leads to errors in more challenging examples of attribution: anaphoric and implicit quotes. |
| Approach: | They propose to augment a popular quotation attribution system, BookNLP, with character embeddings that encode global stylistic information of characters derived from an off-the-shelf stylometric model, Universal Authorship Representation (UAR). |
| Outcome: | The proposed system improves anaphoric and implicit quotes, reaching state-of-the-art. |
Modeling the Music Genre Perception across Language-Bound Cultures (2020.emnlp-main)
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| Challenge: | a prevalent approach to culturally study music genres assumes that the same music genre is associated with the items in all cultures. |
| Approach: | They propose to use distributed concept embeddings and ontologies to obtain cross-lingual music genre annotations using language-specific semantic representations. |
| Outcome: | The proposed model can be compared with existing models using domain-dependent cross-lingual corpus. |
Probing Pre-trained Auto-regressive Language Models for Named Entity Typing and Recognition (2022.lrec-1)
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| Challenge: | Existing studies have focused on auto-regressive models for generalization in named entity (NE) typing (NET) and recognition (NER) . however, little has been done in this direction for auto-Regressive LMs despite their popularity and potential to express a wide variety of NLP tasks in the same unified format. |
| Approach: | They propose to probe auto-regressive LMs for NET and NER generalization by resorting to meta-learning to assess the model's memorization of NEs. |
| Outcome: | The proposed model performs well on NET and NER generalization tasks, while relying more on NE than contextual cues in few-shot NER. |
Evaluating LLMs for Quotation Attribution in Literary Texts: A Case Study of LLaMa3 (2025.naacl-short)
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| Challenge: | Large Language Models (LLMs) have shown promising results in literary tasks . however, quotation attribution remains a challenging task and methods that generalize across writing styles are lacking analysis regarding book memorization and annotation contamination. |
| Approach: | They evaluate the ability of Llama-3 to attribute utterances of direct-speech to their speaker in novels by assessing the impact of book memorization and annotation contamination. |
| Outcome: | The proposed model outperforms existing models on a corpus of 28 novels and shows that book memorization and annotation contamination do not explain the performance gain. |
A Human Subject Study of Named Entity Recognition in Conversational Music Recommendation Queries (2023.eacl-main)
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| Challenge: | NER is a complex task that requires a high degree of precision and a higher level of recall. |
| Approach: | They evaluated the human NER linguistic behaviour on a noisy corpus of conversational music recommendation queries with many irregular and novel named entities. |
| Outcome: | The results show that human NER was hard to perform under a strict evaluation schema and that the model had higher recall because of entity exposure. |
Data-Efficient Playlist Captioning With Musical and Linguistic Knowledge (2022.emnlp-main)
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| Challenge: | Music streaming services feature billions of playlists created by users, professional editors or algorithms. |
| Approach: | They propose a multi-modal encoder-decoder model for automatic playlist captioning that leverages linguistic and musical knowledge to generate correct and thematic captions. |
| Outcome: | The proposed model yields 2x-3x higher BLEU@4 and CIDEr than state-of-the-art captioning algorithms on a new playlists dataset from two major streaming services. |