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        <title>Aiming beyond the Obvious: Identifying Non-Obvious Cases in Semantic Similarity Datasets</title>
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    <abstract>Existing datasets for scoring text pairs in terms of semantic similarity contain instances whose resolution differs according to the degree of difficulty. This paper proposes to distinguish obvious from non-obvious text pairs based on superficial lexical overlap and ground-truth labels. We characterise existing datasets in terms of containing difficult cases and find that recently proposed models struggle to capture the non-obvious cases of semantic similarity. We describe metrics that emphasise cases of similarity which require more complex inference and propose that these are used for evaluating systems for semantic similarity.</abstract>
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%0 Conference Proceedings
%T Aiming beyond the Obvious: Identifying Non-Obvious Cases in Semantic Similarity Datasets
%A Peinelt, Nicole
%A Liakata, Maria
%A Nguyen, Dong
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F peinelt-etal-2019-aiming
%X Existing datasets for scoring text pairs in terms of semantic similarity contain instances whose resolution differs according to the degree of difficulty. This paper proposes to distinguish obvious from non-obvious text pairs based on superficial lexical overlap and ground-truth labels. We characterise existing datasets in terms of containing difficult cases and find that recently proposed models struggle to capture the non-obvious cases of semantic similarity. We describe metrics that emphasise cases of similarity which require more complex inference and propose that these are used for evaluating systems for semantic similarity.
%R 10.18653/v1/P19-1268
%U https://aclanthology.org/P19-1268
%U https://doi.org/10.18653/v1/P19-1268
%P 2792-2798

Markdown (Informal)

[Aiming beyond the Obvious: Identifying Non-Obvious Cases in Semantic Similarity Datasets](https://aclanthology.org/P19-1268) (Peinelt et al., ACL 2019)

• Aiming beyond the Obvious: Identifying Non-Obvious Cases in Semantic Similarity Datasets (Peinelt et al., ACL 2019)

ACL

• Nicole Peinelt, Maria Liakata, and Dong Nguyen. 2019. Aiming beyond the Obvious: Identifying Non-Obvious Cases in Semantic Similarity Datasets. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 2792–2798, Florence, Italy. Association for Computational Linguistics.