Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Unsupervised Discrete Sentence Representation Learning for Interpretable
  Neural Dialog Generation   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.

15
The categories of this paper are: nucl-ex
12
The categories of this paper are: nucl-th
33
The categories of this paper are: plasm-ph
10
The categories of this paper are: q-bio
23
The categories of this paper are: solv-int
6
The categories of this paper are: astro-ph
34
The categories of this paper are: chem-ph
24
The categories of this paper are: dg-ga
8
The categories of this paper are: hep-ex
7
The categories of this paper are: hep-th
31
The categories of this paper are: ao-sci
20
The categories of this paper are: acc-phys
32
The categories of this paper are: atom-ph
37
The categories of this paper are: supr-con
4
The categories of this paper are: cond-mat
25
The categories of this paper are: patt-sol