Unsupervised Discrete Sentence Representation Learning for Interpretable Neural Dialog Generation

TL;DR

This paper introduces unsupervised discrete sentence representations for neural dialog systems, enabling interpretable responses by discovering semantic features without labeled data.

Contribution

It proposes two novel models, DI-VAE and DI-VST, that improve VAEs for discovering interpretable semantics in dialog generation.

Findings

Successfully discovered semantic representations from real-world dialog data.

Enhanced encoder-decoder models with interpretable response generation.

Validated on real-world datasets demonstrating improved interpretability.

Abstract

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.