A generative attentional neural network model for dialogue act classification

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Abstract

We propose a novel generative neural network architecture for Dialogue Act classification. Building upon the Recurrent Neural Network framework, our model incorporates a new attentional technique and a label-to-label connection for sequence learning, akin to Hidden Markov Models. Our experiments show that both of these innovations enable our model to outperform strong baselines for dialogue-act classification on the MapTask and Switchboard corpora. In addition, we analyse empirically the effectiveness of each of these innovations.

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Tran, Q. H., Zukerman, I., & Haffari, G. (2017). A generative attentional neural network model for dialogue act classification. In ACL 2017 - 55th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers) (Vol. 2, pp. 524–529). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/P17-2083

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