Dan Gutfreund


2024

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A Graph per Persona: Reasoning about Subjective Natural Language Descriptions
EunJeong Hwang | Vered Shwartz | Dan Gutfreund | Veronika Thost
Findings of the Association for Computational Linguistics ACL 2024

Reasoning about subjective natural language descriptions, such as opinions and preferences, is a challenging topic that largely remains unsolved to date. In particular, state-of-the-art large language models (LLMs) perform disappointingly in this task, show strong biases, and do not meet the interpretability requirements often needed in these kinds of applications. We propose a novel approach for reasoning about subjective knowledge that integrates potential and implicit meanings and explicitly models the relational nature of the information. We apply supervised graph learning, offer explanations for the model’s reasoning, and show that our model performs well across all 15 topics of OpinionQA, outperforming several prominent LLMs. Our detailed analysis further shows its unique advantages and the complementary nature it offers in comparison to LLMs.

2014

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Claims on demand – an initial demonstration of a system for automatic detection and polarity identification of context dependent claims in massive corpora
Noam Slonim | Ehud Aharoni | Carlos Alzate | Roy Bar-Haim | Yonatan Bilu | Lena Dankin | Iris Eiron | Daniel Hershcovich | Shay Hummel | Mitesh Khapra | Tamar Lavee | Ran Levy | Paul Matchen | Anatoly Polnarov | Vikas Raykar | Ruty Rinott | Amrita Saha | Naama Zwerdling | David Konopnicki | Dan Gutfreund
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: System Demonstrations

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A Benchmark Dataset for Automatic Detection of Claims and Evidence in the Context of Controversial Topics
Ehud Aharoni | Anatoly Polnarov | Tamar Lavee | Daniel Hershcovich | Ran Levy | Ruty Rinott | Dan Gutfreund | Noam Slonim
Proceedings of the First Workshop on Argumentation Mining