Natural Language Generation for Spoken Dialogue System using RNN Encoder-Decoder Networks

Van-Khanh Tran, Le-Minh Nguyen


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.
Anthology ID:
K17-1044
Volume:
Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)
Month:
August
Year:
2017
Address:
Vancouver, Canada
Venue:
CoNLL
SIG:
SIGNLL
Publisher:
Association for Computational Linguistics
Note:
Pages:
442–451
Language:
URL:
https://www.aclweb.org/anthology/K17-1044
DOI:
10.18653/v1/K17-1044
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PDF:
http://aclanthology.lst.uni-saarland.de/K17-1044.pdf