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    <abstract>Cross-domain natural language generation (NLG) is still a difficult task within spoken dialogue modelling. Given a semantic representation provided by the dialogue manager, the language generator should generate sentences that convey desired information. Traditional template-based generators can produce sentences with all necessary information, but these sentences are not sufficiently diverse. With RNN-based models, the diversity of the generated sentences can be high, however, in the process some information is lost. In this work, we improve an RNN-based generator by considering latent information at the sentence level during generation using conditional variational auto-encoder architecture. We demonstrate that our model outperforms the original RNN-based generator, while yielding highly diverse sentences. In addition, our model performs better when the training data is limited.</abstract>
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%0 Conference Proceedings
%T Variational Cross-domain Natural Language Generation for Spoken Dialogue Systems
%A Tseng, Bo-Hsiang
%A Kreyssig, Florian
%A Budzianowski, Paweł
%A Casanueva, Iñigo
%A Wu, Yen-Chen
%A Ultes, Stefan
%A Gašić, Milica
%Y Komatani, Kazunori
%Y Litman, Diane
%Y Yu, Kai
%Y Papangelis, Alex
%Y Cavedon, Lawrence
%Y Nakano, Mikio
%S Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F tseng-etal-2018-variational
%X Cross-domain natural language generation (NLG) is still a difficult task within spoken dialogue modelling. Given a semantic representation provided by the dialogue manager, the language generator should generate sentences that convey desired information. Traditional template-based generators can produce sentences with all necessary information, but these sentences are not sufficiently diverse. With RNN-based models, the diversity of the generated sentences can be high, however, in the process some information is lost. In this work, we improve an RNN-based generator by considering latent information at the sentence level during generation using conditional variational auto-encoder architecture. We demonstrate that our model outperforms the original RNN-based generator, while yielding highly diverse sentences. In addition, our model performs better when the training data is limited.
%R 10.18653/v1/W18-5039
%U https://aclanthology.org/W18-5039
%U https://doi.org/10.18653/v1/W18-5039
%P 338-343

Markdown (Informal)

[Variational Cross-domain Natural Language Generation for Spoken Dialogue Systems](https://aclanthology.org/W18-5039) (Tseng et al., SIGDIAL 2018)

• Variational Cross-domain Natural Language Generation for Spoken Dialogue Systems (Tseng et al., SIGDIAL 2018)

ACL

• Bo-Hsiang Tseng, Florian Kreyssig, Paweł Budzianowski, Iñigo Casanueva, Yen-Chen Wu, Stefan Ultes, and Milica Gašić. 2018. Variational Cross-domain Natural Language Generation for Spoken Dialogue Systems. In Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue, pages 338–343, Melbourne, Australia. Association for Computational Linguistics.