Matthieu Geist


2023

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Factually Consistent Summarization via Reinforcement Learning with Textual Entailment Feedback
Paul Roit | Johan Ferret | Lior Shani | Roee Aharoni | Geoffrey Cideron | Robert Dadashi | Matthieu Geist | Sertan Girgin | Leonard Hussenot | Orgad Keller | Nikola Momchev | Sabela Ramos Garea | Piotr Stanczyk | Nino Vieillard | Olivier Bachem | Gal Elidan | Avinatan Hassidim | Olivier Pietquin | Idan Szpektor
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Despite the seeming success of contemporary grounded text generation systems, they often tend to generate factually inconsistent text with respect to their input. This phenomenon is emphasized in tasks like summarization, in which the generated summaries should be corroborated by their source article. In this work we leverage recent progress on textual entailment models to directly address this problem for abstractive summarization systems. We use reinforcement learning with reference-free, textual-entailment rewards to optimize for factual consistency and explore the ensuing trade-offs, as improved consistency may come at the cost of less informative or more extractive summaries. Our results, according to both automatic metrics and human evaluation, show that our method considerably improves the faithfulness, salience and conciseness of the generated summaries.

2013

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Model-free POMDP optimisation of tutoring systems with echo-state networks
Lucie Daubigney | Matthieu Geist | Olivier Pietquin
Proceedings of the SIGDIAL 2013 Conference

2012

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Optimisation d’un tuteur intelligent à partir d’un jeu de données fixé (Optimization of a tutoring system from a fixed set of data) [in French]
Lucie Daubigney | Matthieu Geist | Olivier Pietquin
Proceedings of the Joint Conference JEP-TALN-RECITAL 2012, volume 1: JEP

2010

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Sparse Approximate Dynamic Programming for Dialog Management
Senthilkumar Chandramohan | Matthieu Geist | Olivier Pietquin
Proceedings of the SIGDIAL 2010 Conference