Amir Cohen


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McPhraSy: Multi-Context Phrase Similarity and Clustering
Amir Cohen | Hila Gonen | Ori Shapira | Ran Levy | Yoav Goldberg
Findings of the Association for Computational Linguistics: EMNLP 2022

Phrase similarity is a key component of many NLP applications. Current phrase similarity methods focus on embedding the phrase itself and use the phrase context only during training of the pretrained model. To better leverage the information in the context, we propose McPhraSy (Multi-context Phrase Similarity), a novel algorithm for estimating the similarity of phrases based on multiple contexts. At inference time, McPhraSy represents each phrase by considering multiple contexts in which it appears and computes the similarity of two phrases by aggregating the pairwise similarities between the contexts of the phrases. Incorporating context during inference enables McPhraSy to outperform current state-of-the-art models on two phrase similarity datasets by up to 13.3%. Finally, we also present a new downstream task that relies on phrase similarity – keyphrase clustering – and create a new benchmark for it in the product reviews domain. We show that McPhraSy surpasses all other baselines for this task.


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Automatic Rephrasing of Transcripts-based Action Items
Amir Cohen | Amir Kantor | Sagi Hilleli | Eyal Kolman
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021