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        <title>Steering Output Style and Topic in Neural Response Generation</title>
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        <namePart type="given">Di</namePart>
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    <abstract>We propose simple and flexible training and decoding methods for influencing output style and topic in neural encoder-decoder based language generation. This capability is desirable in a variety of applications, including conversational systems, where successful agents need to produce language in a specific style and generate responses steered by a human puppeteer or external knowledge. We decompose the neural generation process into empirically easier sub-problems: a faithfulness model and a decoding method based on selective-sampling. We also describe training and sampling algorithms that bias the generation process with a specific language style restriction, or a topic restriction. Human evaluation results show that our proposed methods are able to to restrict style and topic without degrading output quality in conversational tasks.</abstract>
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%0 Conference Proceedings
%T Steering Output Style and Topic in Neural Response Generation
%A Wang, Di
%A Jojic, Nebojsa
%A Brockett, Chris
%A Nyberg, Eric
%Y Palmer, Martha
%Y Hwa, Rebecca
%Y Riedel, Sebastian
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F wang-etal-2017-steering
%X We propose simple and flexible training and decoding methods for influencing output style and topic in neural encoder-decoder based language generation. This capability is desirable in a variety of applications, including conversational systems, where successful agents need to produce language in a specific style and generate responses steered by a human puppeteer or external knowledge. We decompose the neural generation process into empirically easier sub-problems: a faithfulness model and a decoding method based on selective-sampling. We also describe training and sampling algorithms that bias the generation process with a specific language style restriction, or a topic restriction. Human evaluation results show that our proposed methods are able to to restrict style and topic without degrading output quality in conversational tasks.
%R 10.18653/v1/D17-1228
%U https://aclanthology.org/D17-1228
%U https://doi.org/10.18653/v1/D17-1228
%P 2140-2150

Markdown (Informal)

[Steering Output Style and Topic in Neural Response Generation](https://aclanthology.org/D17-1228) (Wang et al., EMNLP 2017)

• Steering Output Style and Topic in Neural Response Generation (Wang et al., EMNLP 2017)

ACL

• Di Wang, Nebojsa Jojic, Chris Brockett, and Eric Nyberg. 2017. Steering Output Style and Topic in Neural Response Generation. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 2140–2150, Copenhagen, Denmark. Association for Computational Linguistics.