Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Disentangling Language and Knowledge in Task-Oriented Dialogs   The Knowledge Base (KB) used for real-world applications, such as booking a
movie or restaurant reservation, keeps changing over time. End-to-end neural
networks trained for these task-oriented dialogs are expected to be immune to
any changes in the KB. However, existing approaches breakdown when asked to
handle such changes. We propose an encoder-decoder architecture (BoSsNet) with
a novel Bag-of-Sequences (BoSs) memory, which facilitates the disentangled
learning of the response's language model and its knowledge incorporation.
Consequently, the KB can be modified with new knowledge without a drop in
interpretability. We find that BoSsNet outperforms state-of-the-art models,
with considerable improvements (> 10\%) on bAbI OOV test sets and other
human-human datasets. We also systematically modify existing datasets to
measure disentanglement and show BoSsNet to be robust to KB modifications.

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The categories of this paper are: atom-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: chao-dyn
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The categories of this paper are: physics
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The categories of this paper are: hep-th
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The categories of this paper are: eess
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The categories of this paper are: econ
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The categories of this paper are: q-bio
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The categories of this paper are: hep-lat
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The categories of this paper are: astro-ph
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The categories of this paper are: patt-sol
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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: gr-qc
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The categories of this paper are: nucl-ex
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The categories of this paper are: solv-int