activity
20242026
collaborators

6 papers

cs.CL2026

Fast and Accurate Quotation Attribution in Literary Texts

Gaspard Michel, Hugo Attali, Elena V. Epure

Attributing quotations to their speakers in literary texts remains an open challenge. Standard methods, which independently predict a speaker mention for each quotation, are effici…

cs.CL2026

GraphLit: Learning Text-Enriched Dynamic Character Network Representations for Literary Study

Gaspard Michel, Elena V. Epure, Romain Hennequin +2

Methods to represent literary texts as graphs or sequences of graphs mainly focus on representing character interactions, and often overlook another crucial aspect: the textual con…

eess.AS2026

Computational Narrative Understanding for Expressive Text-to-Speech

Gaspard Michel, Elena V. Epure, Christophe Cerisara

Recent advances in text-to-speech (TTS) have been driven by large, multi-domain speech corpora, yet the expressive potential of audiobook data remains underexamined. We argue that…

cs.CL2026

S-VoCAL: A Dataset and Evaluation Framework for Inferring Speaking Voice Character Attributes in Literature

Abigail Berthe-Pardo, Gaspard Michel, Elena V. Epure +1

With recent advances in Text-to-Speech (TTS) systems, synthetic audiobook narration has seen increased interest, reaching unprecedented levels of naturalness. However, larger gaps…

cs.CL2025

Evaluating LLMs for Quotation Attribution in Literary Texts: A Case Study of LLaMa3

Gaspard Michel, Elena V. Epure, Romain Hennequin +1

Large Language Models (LLMs) have shown promising results in a variety of literary tasks, often using complex memorized details of narration and fictional characters. In this work,…

cs.CL2024

Improving Quotation Attribution with Fictional Character Embeddings

Gaspard Michel, Elena V. Epure, Romain Hennequin +1

Humans naturally attribute utterances of direct speech to their speaker in literary works. When attributing quotes, we process contextual information but also access mental represe…