SRL4ORL: Improving Opinion Role Labeling using Multi-task Learning with Semantic Role Labeling

NAACL 2018  ·  Ana Marasović, Anette Frank ·

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.

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Results from the Paper

Ranked #2 on Fine-Grained Opinion Analysis on MPQA (using extra training data)

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Task Dataset Model Metric Name Metric Value Global Rank Uses Extra
Training Data
Result Benchmark
Fine-Grained Opinion Analysis MPQA FS-MTL Holder Binary F1 83.80 # 2
Target Binary F1 72.06 # 2


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