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Most Semantic Role Labeling (SRL) approaches are supervised methods whichrequire a significant amount of annotated corpus, and the annotation requireslinguistic expertise. In this paper, we propose a Multi-Task Active Learningframework for Semantic Role Labeling with Entity Recognition (ER) as theauxiliary task to alleviate the need for extensive data and use additionalinformation from ER to help SRL. We evaluate our approach on Indonesianconversational dataset. Our experiments show that multi-task active learningcan outperform single-task active learning method and standard multi-tasklearning. According to our results, active learning is more efficient by using12% less of training data compared to passive learning in both single-task andmulti-task setting. We also introduce a new dataset for SRL in Indonesianconversational domain to encourage further research in this area.

Multi-Task Active Learning for Neural Semantic Role Labeling on Low Resource Conversational Corpus