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        <title>Dialogue-act-driven Conversation Model : An Experimental Study</title>
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        <namePart type="given">Harshit</namePart>
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    <name type="personal">
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    <abstract>The utility of additional semantic information for the task of next utterance selection in an automated dialogue system is the focus of study in this paper. In particular, we show that additional information available in the form of dialogue acts –when used along with context given in the form of dialogue history– improves the performance irrespective of the underlying model being generative or discriminative. In order to show the model agnostic behavior of dialogue acts, we experiment with several well-known models such as sequence-to-sequence encoder-decoder model, hierarchical encoder-decoder model, and Siamese-based models with and without hierarchy; and show that in all models, incorporating dialogue acts improves the performance by a significant margin. We, furthermore, propose a novel way of encoding dialogue act information, and use it along with hierarchical encoder to build a model that can use the sequential dialogue act information in a natural way. Our proposed model achieves an MRR of about 84.8% for the task of next utterance selection on a newly introduced Daily Dialogue dataset, and outperform the baseline models. We also provide a detailed analysis of results including key insights that explain the improvement in MRR because of dialog act information.</abstract>
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%0 Conference Proceedings
%T Dialogue-act-driven Conversation Model : An Experimental Study
%A Kumar, Harshit
%A Agarwal, Arvind
%A Joshi, Sachindra
%Y Bender, Emily M.
%Y Derczynski, Leon
%Y Isabelle, Pierre
%S Proceedings of the 27th International Conference on Computational Linguistics
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico, USA
%F kumar-etal-2018-dialogue
%X The utility of additional semantic information for the task of next utterance selection in an automated dialogue system is the focus of study in this paper. In particular, we show that additional information available in the form of dialogue acts –when used along with context given in the form of dialogue history– improves the performance irrespective of the underlying model being generative or discriminative. In order to show the model agnostic behavior of dialogue acts, we experiment with several well-known models such as sequence-to-sequence encoder-decoder model, hierarchical encoder-decoder model, and Siamese-based models with and without hierarchy; and show that in all models, incorporating dialogue acts improves the performance by a significant margin. We, furthermore, propose a novel way of encoding dialogue act information, and use it along with hierarchical encoder to build a model that can use the sequential dialogue act information in a natural way. Our proposed model achieves an MRR of about 84.8% for the task of next utterance selection on a newly introduced Daily Dialogue dataset, and outperform the baseline models. We also provide a detailed analysis of results including key insights that explain the improvement in MRR because of dialog act information.
%U https://aclanthology.org/C18-1106
%P 1246-1256

Markdown (Informal)

[Dialogue-act-driven Conversation Model : An Experimental Study](https://aclanthology.org/C18-1106) (Kumar et al., COLING 2018)

• Dialogue-act-driven Conversation Model : An Experimental Study (Kumar et al., COLING 2018)

ACL

• Harshit Kumar, Arvind Agarwal, and Sachindra Joshi. 2018. Dialogue-act-driven Conversation Model : An Experimental Study. In Proceedings of the 27th International Conference on Computational Linguistics, pages 1246–1256, Santa Fe, New Mexico, USA. Association for Computational Linguistics.