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        <title>Natural Language Generation for Spoken Dialogue System using RNN Encoder-Decoder Networks</title>
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        <namePart type="given">Van-Khanh</namePart>
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            <namePart type="family">Levy</namePart>
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    <abstract>Natural language generation (NLG) is a critical component in a spoken dialogue system. This paper presents a Recurrent Neural Network based Encoder-Decoder architecture, in which an LSTM-based decoder is introduced to select, aggregate semantic elements produced by an attention mechanism over the input elements, and to produce the required utterances. The proposed generator can be jointly trained both sentence planning and surface realization to produce natural language sentences. The proposed model was extensively evaluated on four different NLG datasets. The experimental results showed that the proposed generators not only consistently outperform the previous methods across all the NLG domains but also show an ability to generalize from a new, unseen domain and learn from multi-domain datasets.</abstract>
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%0 Conference Proceedings
%T Natural Language Generation for Spoken Dialogue System using RNN Encoder-Decoder Networks
%A Tran, Van-Khanh
%A Nguyen, Le-Minh
%Y Levy, Roger
%Y Specia, Lucia
%S Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, Canada
%F tran-nguyen-2017-natural
%X Natural language generation (NLG) is a critical component in a spoken dialogue system. This paper presents a Recurrent Neural Network based Encoder-Decoder architecture, in which an LSTM-based decoder is introduced to select, aggregate semantic elements produced by an attention mechanism over the input elements, and to produce the required utterances. The proposed generator can be jointly trained both sentence planning and surface realization to produce natural language sentences. The proposed model was extensively evaluated on four different NLG datasets. The experimental results showed that the proposed generators not only consistently outperform the previous methods across all the NLG domains but also show an ability to generalize from a new, unseen domain and learn from multi-domain datasets.
%R 10.18653/v1/K17-1044
%U https://aclanthology.org/K17-1044
%U https://doi.org/10.18653/v1/K17-1044
%P 442-451

Markdown (Informal)

[Natural Language Generation for Spoken Dialogue System using RNN Encoder-Decoder Networks](https://aclanthology.org/K17-1044) (Tran & Nguyen, CoNLL 2017)

• Natural Language Generation for Spoken Dialogue System using RNN Encoder-Decoder Networks (Tran & Nguyen, CoNLL 2017)

ACL

• Van-Khanh Tran and Le-Minh Nguyen. 2017. Natural Language Generation for Spoken Dialogue System using RNN Encoder-Decoder Networks. In Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017), pages 442–451, Vancouver, Canada. Association for Computational Linguistics.