Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning to Learn End-to-End Goal-Oriented Dialog From Related Dialog
  Tasks   For each goal-oriented dialog task of interest, large amounts of data need to
be collected for end-to-end learning of a neural dialog system. Collecting that
data is a costly and time-consuming process. Instead, we show that we can use
only a small amount of data, supplemented with data from a related dialog task.
Naively learning from related data fails to improve performance as the related
data can be inconsistent with the target task. We describe a meta-learning
based method that selectively learns from the related dialog task data. Our
approach leads to significant accuracy improvements in an example dialog task.

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The categories of this paper are: q-alg
22
The categories of this paper are: chao-dyn
8
The categories of this paper are: hep-ex
36
The categories of this paper are: cmp-lg
19
The categories of this paper are: eess
34
The categories of this paper are: chem-ph
7
The categories of this paper are: hep-th
23
The categories of this paper are: solv-int
27
The categories of this paper are: comp-gas
6
The categories of this paper are: astro-ph
28
The categories of this paper are: alg-geom
12
The categories of this paper are: nucl-th
2
The categories of this paper are: math
16
The categories of this paper are: stat
35
The categories of this paper are: mtrl-th
5
The categories of this paper are: gr-qc