Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Pseudo Labeling and Negative Feedback Learning for Large-scale
  Multi-label Domain Classification   In large-scale domain classification, an utterance can be handled by multiple
domains with overlapped capabilities. However, only a limited number of
ground-truth domains are provided for each training utterance in practice while
knowing as many as correct target labels is helpful for improving the model
performance. In this paper, given one ground-truth domain for each training
utterance, we regard domains consistently predicted with the highest
confidences as additional pseudo labels for the training. In order to reduce
prediction errors due to incorrect pseudo labels, we leverage utterances with
negative system responses to decrease the confidences of the incorrectly
predicted domains. Evaluating on user utterances from an intelligent
conversational system, we show that the proposed approach significantly
improves the performance of domain classification with hypothesis reranking.

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The categories of this paper are: atom-ph
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The categories of this paper are: quant-ph
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The categories of this paper are: hep-th
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The categories of this paper are: chao-dyn
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The categories of this paper are: plasm-ph
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The categories of this paper are: hep-ph
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The categories of this paper are: comp-gas
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The categories of this paper are: nucl-th
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The categories of this paper are: q-bio
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The categories of this paper are: cond-mat
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The categories of this paper are: math-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: adap-org
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The categories of this paper are: eess
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The categories of this paper are: funct-an
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The categories of this paper are: econ