Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Self-training with Few-shot Rationalization: Teacher Explanations Aid
  Student in Few-shot NLU   While pre-trained language models have obtained state-of-the-art performance
for several natural language understanding tasks, they are quite opaque in
terms of their decision-making process. While some recent works focus on
rationalizing neural predictions by highlighting salient concepts in the text
as justifications or rationales, they rely on thousands of labeled training
examples for both task labels as well as an-notated rationales for every
instance. Such extensive large-scale annotations are infeasible to obtain for
many tasks. To this end, we develop a multi-task teacher-student framework
based on self-training language models with limited task-specific labels and
rationales, and judicious sample selection to learn from informative
pseudo-labeled examples1. We study several characteristics of what constitutes
a good rationale and demonstrate that the neural model performance can be
significantly improved by making it aware of its rationalized predictions,
particularly in low-resource settings. Extensive experiments in several
bench-mark datasets demonstrate the effectiveness of our approach.

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The categories of this paper are: solv-int
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The categories of this paper are: q-fin
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The categories of this paper are: q-alg
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The categories of this paper are: chao-dyn
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The categories of this paper are: nucl-th
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The categories of this paper are: mtrl-th
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The categories of this paper are: math
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The categories of this paper are: hep-ex
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The categories of this paper are: astro-ph
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The categories of this paper are: cmp-lg
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The categories of this paper are: quant-ph
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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: stat
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The categories of this paper are: acc-phys
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The categories of this paper are: econ