Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Simple and Efficient Multi-Task Learning Approach for Conditioned
  Dialogue Generation   Conditioned dialogue generation suffers from the scarcity of labeled
responses. In this work, we exploit labeled non-dialogue text data related to
the condition, which are much easier to collect. We propose a multi-task
learning approach to leverage both labeled dialogue and text data. The 3 tasks
jointly optimize the same pre-trained Transformer -- conditioned dialogue
generation task on the labeled dialogue data, conditioned language encoding
task and conditioned language generation task on the labeled text data.
Experimental results show that our approach outperforms the state-of-the-art
models by leveraging the labeled texts, and it also obtains larger improvement
in performance comparing to the previous methods to leverage text data.

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The categories of this paper are: plasm-ph
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The categories of this paper are: funct-an
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The categories of this paper are: bayes-an
7
The categories of this paper are: hep-th
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The categories of this paper are: hep-lat
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The categories of this paper are: mtrl-th
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The categories of this paper are: supr-con
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The categories of this paper are: dg-ga
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The categories of this paper are: ao-sci
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The categories of this paper are: q-alg
0
The categories of this paper are: hep-ph
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The categories of this paper are: nlin
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The categories of this paper are: patt-sol
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The categories of this paper are: comp-gas
4
The categories of this paper are: cond-mat
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The categories of this paper are: adap-org