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        <title>Sequential Dialogue Context Modeling for Spoken Language Understanding</title>
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            <title>Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue</title>
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            <namePart type="given">Kristiina</namePart>
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    <abstract>Spoken Language Understanding (SLU) is a key component of goal oriented dialogue systems that would parse user utterances into semantic frame representations. Traditionally SLU does not utilize the dialogue history beyond the previous system turn and contextual ambiguities are resolved by the downstream components. In this paper, we explore novel approaches for modeling dialogue context in a recurrent neural network (RNN) based language understanding system. We propose the Sequential Dialogue Encoder Network, that allows encoding context from the dialogue history in chronological order. We compare the performance of our proposed architecture with two context models, one that uses just the previous turn context and another that encodes dialogue context in a memory network, but loses the order of utterances in the dialogue history. Experiments with a multi-domain dialogue dataset demonstrate that the proposed architecture results in reduced semantic frame error rates.</abstract>
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%0 Conference Proceedings
%T Sequential Dialogue Context Modeling for Spoken Language Understanding
%A Bapna, Ankur
%A Tür, Gokhan
%A Hakkani-Tür, Dilek
%A Heck, Larry
%Y Jokinen, Kristiina
%Y Stede, Manfred
%Y DeVault, David
%Y Louis, Annie
%S Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue
%D 2017
%8 August
%I Association for Computational Linguistics
%C Saarbrücken, Germany
%F bapna-etal-2017-sequential
%X Spoken Language Understanding (SLU) is a key component of goal oriented dialogue systems that would parse user utterances into semantic frame representations. Traditionally SLU does not utilize the dialogue history beyond the previous system turn and contextual ambiguities are resolved by the downstream components. In this paper, we explore novel approaches for modeling dialogue context in a recurrent neural network (RNN) based language understanding system. We propose the Sequential Dialogue Encoder Network, that allows encoding context from the dialogue history in chronological order. We compare the performance of our proposed architecture with two context models, one that uses just the previous turn context and another that encodes dialogue context in a memory network, but loses the order of utterances in the dialogue history. Experiments with a multi-domain dialogue dataset demonstrate that the proposed architecture results in reduced semantic frame error rates.
%R 10.18653/v1/W17-5514
%U https://aclanthology.org/W17-5514
%U https://doi.org/10.18653/v1/W17-5514
%P 103-114

Markdown (Informal)

[Sequential Dialogue Context Modeling for Spoken Language Understanding](https://aclanthology.org/W17-5514) (Bapna et al., SIGDIAL 2017)

• Sequential Dialogue Context Modeling for Spoken Language Understanding (Bapna et al., SIGDIAL 2017)

ACL

• Ankur Bapna, Gokhan Tür, Dilek Hakkani-Tür, and Larry Heck. 2017. Sequential Dialogue Context Modeling for Spoken Language Understanding. In Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue, pages 103–114, Saarbrücken, Germany. Association for Computational Linguistics.