Transfer Learning for Causal Sentence Detection

Manolis Kyriakakis, Ion Androutsopoulos, Artur Saudabayev, Joan Ginés i Ametllé


Abstract
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.
Anthology ID:
W19-5031
Volume:
Proceedings of the 18th BioNLP Workshop and Shared Task
Month:
August
Year:
2019
Address:
Florence, Italy
Editors:
Dina Demner-Fushman, Kevin Bretonnel Cohen, Sophia Ananiadou, Junichi Tsujii
Venue:
BioNLP
SIG:
SIGBIOMED
Publisher:
Association for Computational Linguistics
Note:
Pages:
292–297
Language:
URL:
https://aclanthology.org/W19-5031
DOI:
10.18653/v1/W19-5031
Bibkey:
Cite (ACL):
Manolis Kyriakakis, Ion Androutsopoulos, Artur Saudabayev, and Joan Ginés i Ametllé. 2019. Transfer Learning for Causal Sentence Detection. In Proceedings of the 18th BioNLP Workshop and Shared Task, pages 292–297, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
Transfer Learning for Causal Sentence Detection (Kyriakakis et al., BioNLP 2019)
Copy Citation:
PDF:
https://aclanthology.org/W19-5031.pdf
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