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        <title>Linguistically-Informed Specificity and Semantic Plausibility for Dialogue Generation</title>
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    <abstract>Sequence-to-sequence models for open-domain dialogue generation tend to favor generic, uninformative responses. Past work has focused on word frequency-based approaches to improving specificity, such as penalizing responses with only common words. In this work, we examine whether specificity is solely a frequency-related notion and find that more linguistically-driven specificity measures are better suited to improving response informativeness. However, we find that forcing a sequence-to-sequence model to be more specific can expose a host of other problems in the responses, including flawed discourse and implausible semantics. We rerank our model’s outputs using externally-trained classifiers targeting each of these identified factors. Experiments show that our final model using linguistically motivated specificity and plausibility reranking improves the informativeness, reasonableness, and grammatically of responses.</abstract>
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    <identifier type="doi">10.18653/v1/N19-1349</identifier>
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%0 Conference Proceedings
%T Linguistically-Informed Specificity and Semantic Plausibility for Dialogue Generation
%A Ko, Wei-Jen
%A Durrett, Greg
%A Li, Junyi Jessy
%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 ko-etal-2019-linguistically
%X Sequence-to-sequence models for open-domain dialogue generation tend to favor generic, uninformative responses. Past work has focused on word frequency-based approaches to improving specificity, such as penalizing responses with only common words. In this work, we examine whether specificity is solely a frequency-related notion and find that more linguistically-driven specificity measures are better suited to improving response informativeness. However, we find that forcing a sequence-to-sequence model to be more specific can expose a host of other problems in the responses, including flawed discourse and implausible semantics. We rerank our model’s outputs using externally-trained classifiers targeting each of these identified factors. Experiments show that our final model using linguistically motivated specificity and plausibility reranking improves the informativeness, reasonableness, and grammatically of responses.
%R 10.18653/v1/N19-1349
%U https://aclanthology.org/N19-1349
%U https://doi.org/10.18653/v1/N19-1349
%P 3456-3466

Markdown (Informal)

[Linguistically-Informed Specificity and Semantic Plausibility for Dialogue Generation](https://aclanthology.org/N19-1349) (Ko et al., NAACL 2019)

• Linguistically-Informed Specificity and Semantic Plausibility for Dialogue Generation (Ko et al., NAACL 2019)

ACL

• Wei-Jen Ko, Greg Durrett, and Junyi Jessy Li. 2019. Linguistically-Informed Specificity and Semantic Plausibility for Dialogue Generation. 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 3456–3466, Minneapolis, Minnesota. Association for Computational Linguistics.