Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Training a Ranking Function for Open-Domain Question Answering   In recent years, there have been amazing advances in deep learning methods
for machine reading. In machine reading, the machine reader has to extract the
answer from the given ground truth paragraph. Recently, the state-of-the-art
machine reading models achieve human level performance in SQuAD which is a
reading comprehension-style question answering (QA) task. The success of
machine reading has inspired researchers to combine information retrieval with
machine reading to tackle open-domain QA. However, these systems perform poorly
compared to reading comprehension-style QA because it is difficult to retrieve
the pieces of paragraphs that contain the answer to the question. In this
study, we propose two neural network rankers that assign scores to different
passages based on their likelihood of containing the answer to a given
question. Additionally, we analyze the relative importance of semantic
similarity and word level relevance matching in open-domain QA.

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The categories of this paper are: nucl-th
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The categories of this paper are: solv-int
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The categories of this paper are: gr-qc
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The categories of this paper are: dg-ga
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The categories of this paper are: alg-geom
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The categories of this paper are: hep-ex
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The categories of this paper are: adap-org
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The categories of this paper are: quant-ph
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The categories of this paper are: q-fin
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The categories of this paper are: cond-mat
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The categories of this paper are: astro-ph
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The categories of this paper are: econ
35
The categories of this paper are: mtrl-th
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The categories of this paper are: plasm-ph
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The categories of this paper are: q-bio
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The categories of this paper are: cmp-lg