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    <abstract>By design, word embeddings are unable to model the dynamic nature of words’ semantics, i.e., the property of words to correspond to potentially different meanings. To address this limitation, dozens of specialized meaning representation techniques such as sense or contextualized embeddings have been proposed. However, despite the popularity of research on this topic, very few evaluation benchmarks exist that specifically focus on the dynamic semantics of words. In this paper we show that existing models have surpassed the performance ceiling of the standard evaluation dataset for the purpose, i.e., Stanford Contextual Word Similarity, and highlight its shortcomings. To address the lack of a suitable benchmark, we put forward a large-scale Word in Context dataset, called WiC, based on annotations curated by experts, for generic evaluation of context-sensitive representations. WiC is released in https://pilehvar.github.io/wic/.</abstract>
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%0 Conference Proceedings
%T WiC: the Word-in-Context Dataset for Evaluating Context-Sensitive Meaning Representations
%A Pilehvar, Mohammad Taher
%A Camacho-Collados, Jose
%Y Burstein, Jill
%Y Doran, Christy
%Y Solorio, Thamar
%S Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F pilehvar-camacho-collados-2019-wic
%X By design, word embeddings are unable to model the dynamic nature of words’ semantics, i.e., the property of words to correspond to potentially different meanings. To address this limitation, dozens of specialized meaning representation techniques such as sense or contextualized embeddings have been proposed. However, despite the popularity of research on this topic, very few evaluation benchmarks exist that specifically focus on the dynamic semantics of words. In this paper we show that existing models have surpassed the performance ceiling of the standard evaluation dataset for the purpose, i.e., Stanford Contextual Word Similarity, and highlight its shortcomings. To address the lack of a suitable benchmark, we put forward a large-scale Word in Context dataset, called WiC, based on annotations curated by experts, for generic evaluation of context-sensitive representations. WiC is released in https://pilehvar.github.io/wic/.
%R 10.18653/v1/N19-1128
%U https://aclanthology.org/N19-1128
%U https://doi.org/10.18653/v1/N19-1128
%P 1267-1273

Markdown (Informal)

[WiC: the Word-in-Context Dataset for Evaluating Context-Sensitive Meaning Representations](https://aclanthology.org/N19-1128) (Pilehvar & Camacho-Collados, NAACL 2019)

• WiC: the Word-in-Context Dataset for Evaluating Context-Sensitive Meaning Representations (Pilehvar & Camacho-Collados, NAACL 2019)

ACL

• Mohammad Taher Pilehvar and Jose Camacho-Collados. 2019. WiC: the Word-in-Context Dataset for Evaluating Context-Sensitive Meaning Representations. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 1267–1273, Minneapolis, Minnesota. Association for Computational Linguistics.