Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Team Enigma at ArgMining-EMNLP 2021: Leveraging Pre-trained Language
  Models for Key Point Matching   We present the system description for our submission towards the Key Point
Analysis Shared Task at ArgMining 2021. Track 1 of the shared task requires
participants to develop methods to predict the match score between each pair of
arguments and keypoints, provided they belong to the same topic under the same
stance. We leveraged existing state of the art pre-trained language models
along with incorporating additional data and features extracted from the inputs
(topics, key points, and arguments) to improve performance. We were able to
achieve mAP strict and mAP relaxed score of 0.872 and 0.966 respectively in the
evaluation phase, securing 5th place on the leaderboard. In the post evaluation
phase, we achieved a mAP strict and mAP relaxed score of 0.921 and 0.982
respectively. All the codes to generate reproducible results on our models are
available on Github.

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The categories of this paper are: patt-sol
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The categories of this paper are: funct-an
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The categories of this paper are: comp-gas
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The categories of this paper are: stat
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The categories of this paper are: q-bio
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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: q-fin
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The categories of this paper are: chao-dyn
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The categories of this paper are: atom-ph
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The categories of this paper are: q-alg
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The categories of this paper are: econ
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The categories of this paper are: astro-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: bayes-an
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The categories of this paper are: supr-con