Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Training with Streaming Annotation   In this paper, we address a practical scenario where training data is
released in a sequence of small-scale batches and annotation in earlier phases
has lower quality than the later counterparts. To tackle the situation, we
utilize a pre-trained transformer network to preserve and integrate the most
salient document information from the earlier batches while focusing on the
annotation (presumably with higher quality) from the current batch. Using event
extraction as a case study, we demonstrate in the experiments that our proposed
framework can perform better than conventional approaches (the improvement
ranges from 3.6 to 14.9% absolute F-score gain), especially when there is more
noise in the early annotation; and our approach spares 19.1% time with regard
to the best conventional method.

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The categories of this paper are: quant-ph
30
The categories of this paper are: q-alg
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The categories of this paper are: bayes-an
18
The categories of this paper are: econ
11
The categories of this paper are: hep-lat
20
The categories of this paper are: acc-phys
6
The categories of this paper are: astro-ph
32
The categories of this paper are: atom-ph
24
The categories of this paper are: dg-ga
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The categories of this paper are: cmp-lg
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The categories of this paper are: q-bio
29
The categories of this paper are: funct-an
3
The categories of this paper are: physics
34
The categories of this paper are: chem-ph
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The categories of this paper are: nucl-th
33
The categories of this paper are: plasm-ph