Transfer learning for causal sentence detection

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationBioNLP 2019 - SIGBioMed Workshop on Biomedical Natural Language Processing, Proceedings of the 18th BioNLP Workshop and Shared Task
PublisherAssociation for Computational Linguistics (ACL)
Pages292-297
Number of pages6
ISBN (Electronic)9781950737284
Publication statusPublished - 2019
Event18th SIGBioMed Workshop on Biomedical Natural Language Processing, BioNLP 2019 - Florence, Italy
Duration: Aug 1 2019 → …

Publication series

NameBioNLP 2019 - SIGBioMed Workshop on Biomedical Natural Language Processing, Proceedings of the 18th BioNLP Workshop and Shared Task

Conference

Conference18th SIGBioMed Workshop on Biomedical Natural Language Processing, BioNLP 2019
Country/TerritoryItaly
CityFlorence
Period8/1/19 → …

ASJC Scopus subject areas

  • Health Informatics
  • Language and Linguistics
  • Computer Science Applications
  • Information Systems
  • Software
  • Biomedical Engineering

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