Dialogue Act Classification in Domain-Independent Conversations Using a Deep Recurrent Neural Network

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Description

This paper applies a deep long-short term memory (LSTM) structure to classify dialogue acts in open-domain conversations.

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10 p.

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Khanpour, Hamed; Guntakandla, Nishitha & Nielsen, Rodney D. December 2016.

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This paper is part of the collection entitled: UNT Scholarly Works and was provided by UNT College of Engineering to Digital Library, a digital repository hosted by the UNT Libraries. It has been viewed 139 times , with 20 in the last month . More information about this paper can be viewed below.

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Description

This paper applies a deep long-short term memory (LSTM) structure to classify dialogue acts in open-domain conversations.

Physical Description

10 p.

Notes

Abstract: In this study, we applied a deep LSTM structure to classify dialogue acts (DAs) in open-domain conversations. We found that the word embeddings parameters, dropout regularization, decay rate and number of layers are the parameters that have the largest effect on the final system accuracy. Using the findings of these experiments, we trained a deep LSTM network that outperforms the state-of-the-art on the Switchboard corpus by 3.11%, and MRDA by 2.2%.

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  • 26th International Conference on Computational Linguistics, December 11-17, 2016. Osaka, Japan.

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  • Publication Title: Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
  • Pages: 2012-2021
  • Peer Reviewed: Yes

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UNT Scholarly Works

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Creation Date

  • December 2016

Added to The UNT Digital Library

  • Aug. 31, 2017, 5:38 p.m.

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Khanpour, Hamed; Guntakandla, Nishitha & Nielsen, Rodney D. Dialogue Act Classification in Domain-Independent Conversations Using a Deep Recurrent Neural Network, paper, December 2016; Stroudsburg, Pennsylvania. (digital.library.unt.edu/ark:/67531/metadc991476/: accessed September 23, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT College of Engineering.