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        <title>PiC: A Phrase-in-Context Dataset for Phrase Understanding and Semantic Search</title>
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    <abstract>While contextualized word embeddings have been a de-facto standard, learning contextualized phrase embeddings is less explored and being hindered by the lack of a human-annotated benchmark that tests machine understanding of phrase semantics given a context sentence or paragraph (instead of phrases alone). To fill this gap, we propose PiC—a dataset of ∼28K of noun phrases accompanied by their contextual Wikipedia pages and a suite of three tasks for training and evaluating phrase embeddings. Training on PiC improves ranking-models’ accuracy and remarkably pushes span selection (SS) models (i.e., predicting the start and end index of the target phrase) near human accuracy, which is 95% Exact Match (EM) on semantic search given a query phrase and a passage. Interestingly, we find evidence that such impressive performance is because the SS models learn to better capture the common meaning of a phrase regardless of its actual context. SotA models perform poorly in distinguishing two senses of the same phrase in two contexts (∼60% EM) and in estimating the similarity between two different phrases in the same context (∼70% EM).</abstract>
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%0 Conference Proceedings
%T PiC: A Phrase-in-Context Dataset for Phrase Understanding and Semantic Search
%A Pham, Thang
%A Yoon, Seunghyun
%A Bui, Trung
%A Nguyen, Anh
%Y Vlachos, Andreas
%Y Augenstein, Isabelle
%S Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F pham-etal-2023-pic
%X While contextualized word embeddings have been a de-facto standard, learning contextualized phrase embeddings is less explored and being hindered by the lack of a human-annotated benchmark that tests machine understanding of phrase semantics given a context sentence or paragraph (instead of phrases alone). To fill this gap, we propose PiC—a dataset of ∼28K of noun phrases accompanied by their contextual Wikipedia pages and a suite of three tasks for training and evaluating phrase embeddings. Training on PiC improves ranking-models’ accuracy and remarkably pushes span selection (SS) models (i.e., predicting the start and end index of the target phrase) near human accuracy, which is 95% Exact Match (EM) on semantic search given a query phrase and a passage. Interestingly, we find evidence that such impressive performance is because the SS models learn to better capture the common meaning of a phrase regardless of its actual context. SotA models perform poorly in distinguishing two senses of the same phrase in two contexts (∼60% EM) and in estimating the similarity between two different phrases in the same context (∼70% EM).
%R 10.18653/v1/2023.eacl-main.1
%U https://aclanthology.org/2023.eacl-main.1
%U https://doi.org/10.18653/v1/2023.eacl-main.1
%P 1-26

Markdown (Informal)

[PiC: A Phrase-in-Context Dataset for Phrase Understanding and Semantic Search](https://aclanthology.org/2023.eacl-main.1) (Pham et al., EACL 2023)

• PiC: A Phrase-in-Context Dataset for Phrase Understanding and Semantic Search (Pham et al., EACL 2023)

ACL

• Thang Pham, Seunghyun Yoon, Trung Bui, and Anh Nguyen. 2023. PiC: A Phrase-in-Context Dataset for Phrase Understanding and Semantic Search. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, pages 1–26, Dubrovnik, Croatia. Association for Computational Linguistics.