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07-06-2019

End-to-End latent-variable task-oriented dialogue system with exact log-likelihood optimization

Authors: Haotian Xu, Haiyun Peng, Haoran Xie, Erik Cambria, Liuyang Zhou, Weiguo Zheng

Published in: World Wide Web | Issue 3/2020

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Abstract

We propose an end-to-end dialogue model based on a hierarchical encoder-decoder, which employed a discrete latent variable to learn underlying dialogue intentions. The system is able to model the structure of utterances dominated by statistics of the language and the dependencies among utterances in dialogues without manual dialogue state design. We argue that the latent discrete variable interprets the intentions that guide machine responses generation. We also propose a model which can be refined autonomously with reinforcement learning, due to that intention selection at each dialogue turn can be formulated as a sequential decision-making process. Our experiments show that exact MLE optimized model is much more robust than neural variational inference on dialogue success rate with limited BLEU sacrifice.

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Similar to LIDM, all dialogues are pre-processed by delexicalization [9]. Based on the ontology, slot-value specific words are substituted with their corresponding generic tokens.

We concatenate hidden states of bidirectional encoder RNNs of the last step to initialize hidden states of decoder RNNs

Except for training with REINFORCE models

http://gd.xxx.ai/static/chat.html

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Title: End-to-End latent-variable task-oriented dialogue system with exact log-likelihood optimization
Authors: Haotian Xu
Haiyun Peng
Haoran Xie
Erik Cambria
Liuyang Zhou
Weiguo Zheng
Publication date: 07-06-2019
Publisher: Springer US
Published in: World Wide Web / Issue 3/2020
Print ISSN: 1386-145X
Electronic ISSN: 1573-1413
DOI: https://doi.org/10.1007/s11280-019-00688-8

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