@inproceedings{kyriakakis-etal-2019-transfer,
title = "Transfer Learning for Causal Sentence Detection",
author = "Kyriakakis, Manolis and
Androutsopoulos, Ion and
Saudabayev, Artur and
Gin{\'e}s i Ametll{\'e}, Joan",
editor = "Demner-Fushman, Dina and
Cohen, Kevin Bretonnel and
Ananiadou, Sophia and
Tsujii, Junichi",
booktitle = "Proceedings of the 18th BioNLP Workshop and Shared Task",
month = aug,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-5031",
doi = "10.18653/v1/W19-5031",
pages = "292--297",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Transfer Learning for Causal Sentence Detection
%A Kyriakakis, Manolis
%A Androutsopoulos, Ion
%A Saudabayev, Artur
%A Ginés i Ametllé, Joan
%Y Demner-Fushman, Dina
%Y Cohen, Kevin Bretonnel
%Y Ananiadou, Sophia
%Y Tsujii, Junichi
%S Proceedings of the 18th BioNLP Workshop and Shared Task
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F kyriakakis-etal-2019-transfer
%X 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.
%R 10.18653/v1/W19-5031
%U https://aclanthology.org/W19-5031
%U https://doi.org/10.18653/v1/W19-5031
%P 292-297
Markdown (Informal)
[Transfer Learning for Causal Sentence Detection](https://aclanthology.org/W19-5031) (Kyriakakis et al., BioNLP 2019)
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.