Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Simple Approach to Jointly Rank Passages and Select Relevant Sentences
  in the OBQA Context   In the open question answering (OBQA) task, how to select the relevant
information from a large corpus is a crucial problem for reasoning and
inference. Some datasets (e.g, HotpotQA) mainly focus on testing the model's
reasoning ability at the sentence level. To overcome this challenge, many
existing frameworks use a deep learning model to select relevant passages and
then answer each question by matching a sentence in the corresponding passage.
However, such frameworks require long inference time and fail to take advantage
of the relationship between passages and sentences. In this work, we present a
simple yet effective framework to address these problems by jointly ranking
passages and selecting sentences. We propose consistency and similarity
constraints to promote the correlation and interaction between passage ranking
and sentence selection. In our experiments, we demonstrate that our framework
can achieve competitive results and outperform the baseline by 28\% in terms of
exact matching of relevant sentences on the HotpotQA dataset.

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The categories of this paper are: supr-con
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The categories of this paper are: gr-qc
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The categories of this paper are: math
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The categories of this paper are: hep-lat
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The categories of this paper are: acc-phys
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The categories of this paper are: quant-ph
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The categories of this paper are: q-alg
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The categories of this paper are: ao-sci
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The categories of this paper are: chem-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: nlin
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The categories of this paper are: econ
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The categories of this paper are: patt-sol
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The categories of this paper are: eess
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The categories of this paper are: cmp-lg
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The categories of this paper are: comp-gas