Gal Elidan


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.

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Covering Uncommon Ground: Gap-Focused Question Generation for Answer Assessment
Roni Rabin | Alexandre Djerbetian | Roee Engelberg | Lidan Hackmon | Gal Elidan | Reut Tsarfaty | Amir Globerson
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Human communication often involves information gaps between the interlocutors. For example, in an educational dialogue a student often provides an answer that is incomplete, and there is a gap between this answer and the perfect one expected by the teacher. Successful dialogue then hinges on the teacher asking about this gap in an effective manner, thus creating a rich and interactive educational experience. We focus on the problem of generating such gap-focused questions (GFQs) automatically. We define the task, highlight key desired aspects of a good GFQ, and propose a model that satisfies these. Finally, we provide an evaluation by human annotators of our generated questions compared against human generated ones, demonstrating competitive performance.

2012

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A Diverse Dirichlet Process Ensemble for Unsupervised Induction of Syntactic Categories
Roi Reichart | Gal Elidan | Ari Rappoport
Proceedings of COLING 2012