Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Effectiveness of Pre-training for Few-shot Intent Classification   This paper investigates the effectiveness of pre-training for few-shot intent
classification. While existing paradigms commonly further pre-train language
models such as BERT on a vast amount of unlabeled corpus, we find it highly
effective and efficient to simply fine-tune BERT with a small set of labeled
utterances from public datasets. Specifically, fine-tuning BERT with roughly
1,000 labeled data yields a pre-trained model -- IntentBERT, which can easily
surpass the performance of existing pre-trained models for few-shot intent
classification on novel domains with very different semantics. The high
effectiveness of IntentBERT confirms the feasibility and practicality of
few-shot intent detection, and its high generalization ability across different
domains suggests that intent classification tasks may share a similar
underlying structure, which can be efficiently learned from a small set of
labeled data. The source code can be found at
https://github.com/hdzhang-code/IntentBERT.

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The categories of this paper are: mtrl-th
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The categories of this paper are: nucl-ex
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The categories of this paper are: physics
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The categories of this paper are: q-fin
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The categories of this paper are: econ
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The categories of this paper are: atom-ph
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The categories of this paper are: math-ph
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The categories of this paper are: gr-qc
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The categories of this paper are: eess
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The categories of this paper are: hep-lat
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The categories of this paper are: plasm-ph
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The categories of this paper are: cond-mat
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The categories of this paper are: hep-ex
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The categories of this paper are: alg-geom
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The categories of this paper are: cmp-lg
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The categories of this paper are: acc-phys