Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Multi-Granularity Semantic Aware Graph Model for Reducing Position Bias
  in Emotion-Cause Pair Extraction   The Emotion-Cause Pair Extraction (ECPE) task aims to extract emotions and
causes as pairs from documents. We observe that the relative distance
distribution of emotions and causes is extremely imbalanced in the typical ECPE
dataset. Existing methods have set a fixed size window to capture relations
between neighboring clauses. However, they neglect the effective semantic
connections between distant clauses, leading to poor generalization ability
towards position-insensitive data. To alleviate the problem, we propose a novel
\textbf{M}ulti-\textbf{G}ranularity \textbf{S}emantic \textbf{A}ware
\textbf{G}raph model (MGSAG) to incorporate fine-grained and coarse-grained
semantic features jointly, without regard to distance limitation. In
particular, we first explore semantic dependencies between clauses and keywords
extracted from the document that convey fine-grained semantic features,
obtaining keywords enhanced clause representations. Besides, a clause graph is
also established to model coarse-grained semantic relations between clauses.
Experimental results indicate that MGSAG surpasses the existing
state-of-the-art ECPE models. Especially, MGSAG outperforms other models
significantly in the condition of position-insensitive data.

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The categories of this paper are: quant-ph
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The categories of this paper are: cmp-lg
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The categories of this paper are: comp-gas
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The categories of this paper are: bayes-an
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The categories of this paper are: chem-ph
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The categories of this paper are: q-alg
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The categories of this paper are: hep-lat
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The categories of this paper are: chao-dyn
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The categories of this paper are: nlin
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The categories of this paper are: hep-th
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The categories of this paper are: astro-ph
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The categories of this paper are: alg-geom
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The categories of this paper are: patt-sol
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The categories of this paper are: math
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The categories of this paper are: adap-org
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The categories of this paper are: solv-int