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Natural language generation for spoken dialogue system using RNN encoder-decoder networks
conference contribution
posted on 2017-01-01, 00:00 authored by Van Khanh Tran, Le-Minh NguyenNatural 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.
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.
