Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Adapting BERT for Word Sense Disambiguation with Gloss Selection
  Objective and Example Sentences   Domain adaptation or transfer learning using pre-trained language models such
as BERT has proven to be an effective approach for many natural language
processing tasks. In this work, we propose to formulate word sense
disambiguation as a relevance ranking task, and fine-tune BERT on sequence-pair
ranking task to select the most probable sense definition given a context
sentence and a list of candidate sense definitions. We also introduce a data
augmentation technique for WSD using existing example sentences from WordNet.
Using the proposed training objective and data augmentation technique, our
models are able to achieve state-of-the-art results on the English all-words
benchmark datasets.

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The categories of this paper are: ao-sci
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The categories of this paper are: hep-ex
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The categories of this paper are: stat
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The categories of this paper are: nlin
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The categories of this paper are: math-ph
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The categories of this paper are: comp-gas
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The categories of this paper are: solv-int
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The categories of this paper are: funct-an
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The categories of this paper are: chao-dyn
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The categories of this paper are: acc-phys
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The categories of this paper are: econ
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The categories of this paper are: plasm-ph
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The categories of this paper are: q-fin
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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: physics