Papers › PiC: A Phrase-in-Context Dataset for Phrase Understanding and Semantic Search

PiC: A Phrase-in-Context Dataset for Phrase Understanding and Semantic Search

19 Jul 2022arXiv:2207.09068archive 2025-07-28

Thang M. Pham, Seunghyun Yoon, Trung Bui, Anh Nguyen

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).

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Information Retrieval Natural Language Understanding Question Answering Semantic Retrieval Semantic Similarity Sentence text similarity

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Phrase-in-Context

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Adam Attention Attention Dropout BERT Dense Connections Dropout Layer Normalization Linear Layer Linear Warmup With Linear Decay Multi-Head Attention Residual Connection Softmax Weight Decay WordPiece

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