PiC: A Phrase-in-Context Dataset for Phrase Understanding and Semantic Search (2023.eacl-main)
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| Challenge: | Existing benchmarks for phrase-similarity compare phrases alone (without context) and phrases with context (with or without context). |
| Approach: | They propose to use a dataset of 28K noun phrases accompanied by their contextual Wikipedia pages to train machine phrase embeddings. |
| Outcome: | The proposed dataset improves ranking-models’ accuracy and pushes span selection models near human accuracy, which is 95% Exact Match (EM) on semantic search given a query phrase and a passage. |
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Carlos Santos Armendariz, Matthew Purver, Matej Ulčar, Senja Pollak, Nikola Ljubešić, Mark Granroth-Wilding
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