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Natural Language Generation for Spoken Dialogue System using RNN Encoder-Decoder Networks
Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)
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 proposeddoi:10.18653/v1/k17-1044 dblp:conf/conll/TranN17 fatcat:5h4aqchajbbbzb4aiuainnke64