Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Transfer Learning for Causal Sentence Detection   We consider the task of detecting sentences that express causality, as a step
towards mining causal relations from texts. To bypass the scarcity of causal
instances in relation extraction datasets, we exploit transfer learning, namely
ELMO and BERT, using a bidirectional GRU with self-attention (BIGRUATT) as a
baseline. We experiment with both generic public relation extraction datasets
and a new biomedical causal sentence detection dataset, a subset of which we
make publicly available. We find that transfer learning helps only in very
small datasets. With larger datasets, BIGRUATT reaches a performance plateau,
then larger datasets and transfer learning do not help.

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The categories of this paper are: mtrl-th
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The categories of this paper are: dg-ga
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The categories of this paper are: solv-int
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The categories of this paper are: adap-org
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The categories of this paper are: nucl-ex
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The categories of this paper are: plasm-ph
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The categories of this paper are: math-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: ao-sci
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The categories of this paper are: hep-ex
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The categories of this paper are: hep-lat
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The categories of this paper are: stat
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The categories of this paper are: hep-th
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The categories of this paper are: chem-ph
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The categories of this paper are: comp-gas
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The categories of this paper are: alg-geom