Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Key-Value Retrieval Networks for Task-Oriented Dialogue   Neural task-oriented dialogue systems often struggle to smoothly interface
with a knowledge base. In this work, we seek to address this problem by
proposing a new neural dialogue agent that is able to effectively sustain
grounded, multi-domain discourse through a novel key-value retrieval mechanism.
The model is end-to-end differentiable and does not need to explicitly model
dialogue state or belief trackers. We also release a new dataset of 3,031
dialogues that are grounded through underlying knowledge bases and span three
distinct tasks in the in-car personal assistant space: calendar scheduling,
weather information retrieval, and point-of-interest navigation. Our
architecture is simultaneously trained on data from all domains and
significantly outperforms a competitive rule-based system and other existing
neural dialogue architectures on the provided domains according to both
automatic and human evaluation metrics.

31
The categories of this paper are: ao-sci
14
The categories of this paper are: math-ph
28
The categories of this paper are: alg-geom
26
The categories of this paper are: bayes-an
18
The categories of this paper are: econ
32
The categories of this paper are: atom-ph
12
The categories of this paper are: nucl-th
22
The categories of this paper are: chao-dyn
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The categories of this paper are: patt-sol
13
The categories of this paper are: quant-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: eess
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The categories of this paper are: q-fin
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The categories of this paper are: hep-lat
4
The categories of this paper are: cond-mat
16
The categories of this paper are: stat