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Unsupervised Discrete Sentence Representation Learning for Interpretable Neural Dialog Generation
lib:4b42a026b79d386a (v1.0.0)
Authors: Tiancheng Zhao,Kyusong Lee,Maxine Eskenazi
Where published: ACL 2018 7
ArXiv: 1804.08069
Document: PDF DOI
Artifact development version: GitHub
Abstract URL: http://arxiv.org/abs/1804.08069v1
The encoder-decoder dialog model is one of the most prominent methods used to build dialog systems in complex domains. Yet it is limited because it cannot output interpretable actions as in traditional systems, which hinders humans from understanding its generation process. We present an unsupervised discrete sentence representation learning method that can integrate with any existing encoder-decoder dialog models for interpretable response generation. Building upon variational autoencoders (VAEs), we present two novel models, DI-VAE and DI-VST that improve VAEs and can discover interpretable semantics via either auto encoding or context predicting. Our methods have been validated on real-world dialog datasets to discover semantic representations and enhance encoder-decoder models with interpretable generation.
Where published: ACL 2018 7
ArXiv: 1804.08069
Document: PDF DOI
Artifact development version: GitHub
Abstract URL: http://arxiv.org/abs/1804.08069v1
The encoder-decoder dialog model is one of the most prominent methods used to build dialog systems in complex domains. Yet it is limited because it cannot output interpretable actions as in traditional systems, which hinders humans from understanding its generation process. We present an unsupervised discrete sentence representation learning method that can integrate with any existing encoder-decoder dialog models for interpretable response generation. Building upon variational autoencoders (VAEs), we present two novel models, DI-VAE and DI-VST that improve VAEs and can discover interpretable semantics via either auto encoding or context predicting. Our methods have been validated on real-world dialog datasets to discover semantic representations and enhance encoder-decoder models with interpretable generation.
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