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For over 12 years, machine learning is used to extract opinion-holder-target structures from text to answer the question: Who expressed what kind of sentiment towards what?. However, recent neural approaches do not outperform the state-of-the-art feature-based model for Opinion Role Labelling (ORL). We suspect this is due to the scarcity of labelled training data and address this issue using different multi-task learning techniques with a related task which has substantially more data, i.e. Semantic Role Labelling (SRL). Despite difficulties of the benchmark MPQA corpus, we show that indeed the ORL model benefits from SRL knowledge.

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