Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Looking Beyond Label Noise: Shifted Label Distribution Matters in
  Distantly Supervised Relation Extraction   In recent years there is a surge of interest in applying distant supervision
(DS) to automatically generate training data for relation extraction (RE). In
this paper, we study the problem what limits the performance of DS-trained
neural models, conduct thorough analyses, and identify a factor that can
influence the performance greatly, shifted label distribution. Specifically, we
found this problem commonly exists in real-world DS datasets, and without
special handing, typical DS-RE models cannot automatically adapt to this shift,
thus achieving deteriorated performance. To further validate our intuition, we
develop a simple yet effective adaptation method for DS-trained models, bias
adjustment, which updates models learned over the source domain (i.e., DS
training set) with a label distribution estimated on the target domain (i.e.,
test set). Experiments demonstrate that bias adjustment achieves consistent
performance gains on DS-trained models, especially on neural models, with an up
to 23% relative F1 improvement, which verifies our assumptions. Our code and
data can be found at
\url{https://github.com/INK-USC/shifted-label-distribution}.

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The categories of this paper are: plasm-ph
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The categories of this paper are: chem-ph
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The categories of this paper are: alg-geom
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The categories of this paper are: solv-int
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The categories of this paper are: math-ph
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The categories of this paper are: cmp-lg
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The categories of this paper are: nucl-ex
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The categories of this paper are: nucl-th
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The categories of this paper are: physics
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The categories of this paper are: chao-dyn
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The categories of this paper are: q-bio
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The categories of this paper are: funct-an
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The categories of this paper are: nlin
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The categories of this paper are: acc-phys
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The categories of this paper are: hep-lat
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The categories of this paper are: q-alg