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        <title>Automatic Evaluate Dialogue Appropriateness by Using Dialogue Act</title>
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    <abstract>Evaluation of dialogue systems requires assessing various aspects, among which appropriateness holds significance as a core element of communicative language competence. However, current evaluations heavily rely on human judgments, which are time-consuming, labor-intensive, prone to biases, and lacking objectivity. In this paper, we introduce Dialogue Act Appropriateness (DAA), a novel method that utilizes the underlying patterns of dialogue act transitions to evaluate the appropriateness of chatbot responses. We learn transition patterns from human-human dialogue corpora, evaluating chatbot appropriateness by measuring the similarity of their transition patterns to those observed in human-human dialogues. To validate DAA, we annotate a test dataset by manually evaluating the appropriateness of dialogues from multiple chatbot systems. The experimental results demonstrate a strong correlation between our evaluation metric and human ratings, establishing the reliability of DAA as a measure of dialogue appropriateness.</abstract>
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%0 Conference Proceedings
%T Automatic Evaluate Dialogue Appropriateness by Using Dialogue Act
%A Chen, Bao
%A Wang, Yuanjie
%A Liu, Zeming
%A Guo, Yuhang
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F chen-etal-2023-automatic
%X Evaluation of dialogue systems requires assessing various aspects, among which appropriateness holds significance as a core element of communicative language competence. However, current evaluations heavily rely on human judgments, which are time-consuming, labor-intensive, prone to biases, and lacking objectivity. In this paper, we introduce Dialogue Act Appropriateness (DAA), a novel method that utilizes the underlying patterns of dialogue act transitions to evaluate the appropriateness of chatbot responses. We learn transition patterns from human-human dialogue corpora, evaluating chatbot appropriateness by measuring the similarity of their transition patterns to those observed in human-human dialogues. To validate DAA, we annotate a test dataset by manually evaluating the appropriateness of dialogues from multiple chatbot systems. The experimental results demonstrate a strong correlation between our evaluation metric and human ratings, establishing the reliability of DAA as a measure of dialogue appropriateness.
%R 10.18653/v1/2023.findings-emnlp.492
%U https://aclanthology.org/2023.findings-emnlp.492
%U https://doi.org/10.18653/v1/2023.findings-emnlp.492
%P 7361-7372

Markdown (Informal)

[Automatic Evaluate Dialogue Appropriateness by Using Dialogue Act](https://aclanthology.org/2023.findings-emnlp.492) (Chen et al., Findings 2023)

• Automatic Evaluate Dialogue Appropriateness by Using Dialogue Act (Chen et al., Findings 2023)

ACL

• Bao Chen, Yuanjie Wang, Zeming Liu, and Yuhang Guo. 2023. Automatic Evaluate Dialogue Appropriateness by Using Dialogue Act. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 7361–7372, Singapore. Association for Computational Linguistics.