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        <title>Filtering Noisy Dialogue Corpora by Connectivity and Content Relatedness</title>
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        <namePart type="given">Reina</namePart>
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    <abstract>Large-scale dialogue datasets have recently become available for training neural dialogue agents. However, these datasets have been reported to contain a non-negligible number of unacceptable utterance pairs. In this paper, we propose a method for scoring the quality of utterance pairs in terms of their connectivity and relatedness. The proposed scoring method is designed based on findings widely shared in the dialogue and linguistics research communities. We demonstrate that it has a relatively good correlation with the human judgment of dialogue quality. Furthermore, the method is applied to filter out potentially unacceptable utterance pairs from a large-scale noisy dialogue corpus to ensure its quality. We experimentally confirm that training data filtered by the proposed method improves the quality of neural dialogue agents in response generation.</abstract>
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%0 Conference Proceedings
%T Filtering Noisy Dialogue Corpora by Connectivity and Content Relatedness
%A Akama, Reina
%A Yokoi, Sho
%A Suzuki, Jun
%A Inui, Kentaro
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F akama-etal-2020-filtering
%X Large-scale dialogue datasets have recently become available for training neural dialogue agents. However, these datasets have been reported to contain a non-negligible number of unacceptable utterance pairs. In this paper, we propose a method for scoring the quality of utterance pairs in terms of their connectivity and relatedness. The proposed scoring method is designed based on findings widely shared in the dialogue and linguistics research communities. We demonstrate that it has a relatively good correlation with the human judgment of dialogue quality. Furthermore, the method is applied to filter out potentially unacceptable utterance pairs from a large-scale noisy dialogue corpus to ensure its quality. We experimentally confirm that training data filtered by the proposed method improves the quality of neural dialogue agents in response generation.
%R 10.18653/v1/2020.emnlp-main.68
%U https://aclanthology.org/2020.emnlp-main.68
%U https://doi.org/10.18653/v1/2020.emnlp-main.68
%P 941-958

Markdown (Informal)

[Filtering Noisy Dialogue Corpora by Connectivity and Content Relatedness](https://aclanthology.org/2020.emnlp-main.68) (Akama et al., EMNLP 2020)

• Filtering Noisy Dialogue Corpora by Connectivity and Content Relatedness (Akama et al., EMNLP 2020)

ACL

• Reina Akama, Sho Yokoi, Jun Suzuki, and Kentaro Inui. 2020. Filtering Noisy Dialogue Corpora by Connectivity and Content Relatedness. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 941–958, Online. Association for Computational Linguistics.