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
- 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)
- PDF:
- https://preview.aclanthology.org/naacl24-info/W19-5031.pdf
- Code
- additional community code