Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Span-ConveRT: Few-shot Span Extraction for Dialog with Pretrained
  Conversational Representations   We introduce Span-ConveRT, a light-weight model for dialog slot-filling which
frames the task as a turn-based span extraction task. This formulation allows
for a simple integration of conversational knowledge coded in large pretrained
conversational models such as ConveRT (Henderson et al., 2019). We show that
leveraging such knowledge in Span-ConveRT is especially useful for few-shot
learning scenarios: we report consistent gains over 1) a span extractor that
trains representations from scratch in the target domain, and 2) a BERT-based
span extractor. In order to inspire more work on span extraction for the
slot-filling task, we also release RESTAURANTS-8K, a new challenging data set
of 8,198 utterances, compiled from actual conversations in the restaurant
booking domain.

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The categories of this paper are: q-fin
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The categories of this paper are: nlin
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The categories of this paper are: patt-sol
14
The categories of this paper are: math-ph
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The categories of this paper are: nucl-ex
20
The categories of this paper are: acc-phys
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The categories of this paper are: cond-mat
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The categories of this paper are: funct-an
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The categories of this paper are: ao-sci
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The categories of this paper are: chem-ph
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The categories of this paper are: bayes-an
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The categories of this paper are: comp-gas
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The categories of this paper are: supr-con
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The categories of this paper are: gr-qc
13
The categories of this paper are: quant-ph
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The categories of this paper are: cmp-lg