SRL4ORL: Improving Opinion Role Labeling Using Multi-Task Learning with Semantic Role Labeling

Ana Marasović, Anette Frank


Abstract
For over a decade, machine learning has been used to extract opinion-holder-target structures from text to answer the question “Who expressed what kind of sentiment towards what?”. Recent neural approaches do not outperform the state-of-the-art feature-based models for Opinion Role Labeling (ORL). We suspect this is due to the scarcity of labeled training data and address this issue using different multi-task learning (MTL) techniques with a related task which has substantially more data, i.e. Semantic Role Labeling (SRL). We show that two MTL models improve significantly over the single-task model for labeling of both holders and targets, on the development and the test sets. We found that the vanilla MTL model, which makes predictions using only shared ORL and SRL features, performs the best. With deeper analysis we determine what works and what might be done to make further improvements for ORL.
Anthology ID:
N18-1054
Volume:
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
583–594
Language:
URL:
https://www.aclweb.org/anthology/N18-1054
DOI:
10.18653/v1/N18-1054
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PDF:
http://aclanthology.lst.uni-saarland.de/N18-1054.pdf
Note:
 N18-1054.Notes.pdf