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        <title>Sequence-to-Sequence Data Augmentation for Dialogue Language Understanding</title>
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    <abstract>In this paper, we study the problem of data augmentation for language understanding in task-oriented dialogue system. In contrast to previous work which augments an utterance without considering its relation with other utterances, we propose a sequence-to-sequence generation based data augmentation framework that leverages one utterance’s same semantic alternatives in the training data. A novel diversity rank is incorporated into the utterance representation to make the model produce diverse utterances and these diversely augmented utterances help to improve the language understanding module. Experimental results on the Airline Travel Information System dataset and a newly created semantic frame annotation on Stanford Multi-turn, Multi-domain Dialogue Dataset show that our framework achieves significant improvements of 6.38 and 10.04 F-scores respectively when only a training set of hundreds utterances is represented. Case studies also confirm that our method generates diverse utterances.</abstract>
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%0 Conference Proceedings
%T Sequence-to-Sequence Data Augmentation for Dialogue Language Understanding
%A Hou, Yutai
%A Liu, Yijia
%A Che, Wanxiang
%A Liu, Ting
%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 hou-etal-2018-sequence
%X In this paper, we study the problem of data augmentation for language understanding in task-oriented dialogue system. In contrast to previous work which augments an utterance without considering its relation with other utterances, we propose a sequence-to-sequence generation based data augmentation framework that leverages one utterance’s same semantic alternatives in the training data. A novel diversity rank is incorporated into the utterance representation to make the model produce diverse utterances and these diversely augmented utterances help to improve the language understanding module. Experimental results on the Airline Travel Information System dataset and a newly created semantic frame annotation on Stanford Multi-turn, Multi-domain Dialogue Dataset show that our framework achieves significant improvements of 6.38 and 10.04 F-scores respectively when only a training set of hundreds utterances is represented. Case studies also confirm that our method generates diverse utterances.
%U https://aclanthology.org/C18-1105
%P 1234-1245

Markdown (Informal)

[Sequence-to-Sequence Data Augmentation for Dialogue Language Understanding](https://aclanthology.org/C18-1105) (Hou et al., COLING 2018)

• Sequence-to-Sequence Data Augmentation for Dialogue Language Understanding (Hou et al., COLING 2018)

ACL

• Yutai Hou, Yijia Liu, Wanxiang Che, and Ting Liu. 2018. Sequence-to-Sequence Data Augmentation for Dialogue Language Understanding. In Proceedings of the 27th International Conference on Computational Linguistics, pages 1234–1245, Santa Fe, New Mexico, USA. Association for Computational Linguistics.