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        <title>Multilevel Heuristics for Rationale-Based Entity Relation Classification in Sentences</title>
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        <namePart type="given">Shiou</namePart>
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    <name type="personal">
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            <title>Proceedings of the 27th International Conference on Computational Linguistics</title>
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    <abstract>Rationale-based models provide a unique way to provide justifiable results for relation classification models by identifying rationales (key words and phrases that a person can use to justify the relation in the sentence) during the process. However, existing generative networks used to extract rationales come with a trade-off between extracting diversified rationales and achieving good classification results. In this paper, we propose a multilevel heuristic approach to regulate rationale extraction to avoid extracting monotonous rationales without compromising classification performance. In our model, rationale selection is regularized by a semi-supervised process and features from different levels: word, syntax, sentence, and corpus. We evaluate our approach on the SemEval 2010 dataset that includes 19 relation classes and the quality of extracted rationales with our manually-labeled rationales. Experiments show a significant improvement in classification performance and a 20% gain in rationale interpretability compared to state-of-the-art approaches.</abstract>
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        <url>https://aclanthology.org/C18-1098/</url>
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        <date>2018-08</date>
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%0 Conference Proceedings
%T Multilevel Heuristics for Rationale-Based Entity Relation Classification in Sentences
%A Hsu, Shiou Tian
%A Chaudhary, Mandar
%A Samatova, Nagiza
%Y Bender, Emily M.
%Y Derczynski, Leon
%Y Isabelle, Pierre
%S Proceedings of the 27th International Conference on Computational Linguistics
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico, USA
%F hsu-etal-2018-multilevel
%X Rationale-based models provide a unique way to provide justifiable results for relation classification models by identifying rationales (key words and phrases that a person can use to justify the relation in the sentence) during the process. However, existing generative networks used to extract rationales come with a trade-off between extracting diversified rationales and achieving good classification results. In this paper, we propose a multilevel heuristic approach to regulate rationale extraction to avoid extracting monotonous rationales without compromising classification performance. In our model, rationale selection is regularized by a semi-supervised process and features from different levels: word, syntax, sentence, and corpus. We evaluate our approach on the SemEval 2010 dataset that includes 19 relation classes and the quality of extracted rationales with our manually-labeled rationales. Experiments show a significant improvement in classification performance and a 20% gain in rationale interpretability compared to state-of-the-art approaches.
%U https://aclanthology.org/C18-1098/
%P 1145-1155

Markdown (Informal)

[Multilevel Heuristics for Rationale-Based Entity Relation Classification in Sentences](https://aclanthology.org/C18-1098/) (Hsu et al., COLING 2018)

• Multilevel Heuristics for Rationale-Based Entity Relation Classification in Sentences (Hsu et al., COLING 2018)

ACL

• Shiou Tian Hsu, Mandar Chaudhary, and Nagiza Samatova. 2018. Multilevel Heuristics for Rationale-Based Entity Relation Classification in Sentences. In Proceedings of the 27th International Conference on Computational Linguistics, pages 1145–1155, Santa Fe, New Mexico, USA. Association for Computational Linguistics.