Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Mixture Content Selection for Diverse Sequence Generation   Generating diverse sequences is important in many NLP applications such as
question generation or summarization that exhibit semantically one-to-many
relationships between source and the target sequences. We present a method to
explicitly separate diversification from generation using a general
plug-and-play module (called SELECTOR) that wraps around and guides an existing
encoder-decoder model. The diversification stage uses a mixture of experts to
sample different binary masks on the source sequence for diverse content
selection. The generation stage uses a standard encoder-decoder model given
each selected content from the source sequence. Due to the non-differentiable
nature of discrete sampling and the lack of ground truth labels for binary
mask, we leverage a proxy for ground truth mask and adopt stochastic hard-EM
for training. In question generation (SQuAD) and abstractive summarization
(CNN-DM), our method demonstrates significant improvements in accuracy,
diversity and training efficiency, including state-of-the-art top-1 accuracy in
both datasets, 6% gain in top-5 accuracy, and 3.7 times faster training over a
state of the art model. Our code is publicly available at
https://github.com/clovaai/FocusSeq2Seq.

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The categories of this paper are: comp-gas
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The categories of this paper are: math
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The categories of this paper are: econ
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The categories of this paper are: adap-org
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The categories of this paper are: math-ph
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The categories of this paper are: cond-mat
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The categories of this paper are: q-bio
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The categories of this paper are: nucl-ex
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The categories of this paper are: hep-th
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The categories of this paper are: dg-ga
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The categories of this paper are: chem-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: gr-qc
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The categories of this paper are: mtrl-th
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The categories of this paper are: funct-an