Abstract
This paper proposes a method to exploit and integrate dialogue context information into two neural models for dialogue act detection, including recurrent networks and transformers networks. The proposed models are evaluated on two standard benchmark datasets for English and Vietnamese. Extensive experimental results show that our method achieves a significant better performance in comparison to baseline results. Our proposed method has been deployed as a core component of a commercial conversational platform, effectively serving millions of clients in multiple markets.
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Notes
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Note that in transformers-based models, the hidden size must be a multiple of the number of self attention heads.
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https://github.com/phuonglh/vlp/, under the woz module.
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Detailed experimental results and code are available on the GitHub repository.
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Vu, HY., Le-Hong, P. (2023). A Context-Aware Approach for Improving Dialog Act Detection in a Multilingual Conversational Platform. In: Nguyen, N.T., et al. Computational Collective Intelligence. ICCCI 2023. Lecture Notes in Computer Science(), vol 14162. Springer, Cham. https://doi.org/10.1007/978-3-031-41456-5_18
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