Most Semantic Role Labeling (SRL) approaches are supervised methods which require a significant amount of annotated corpus, and the annotation requires linguistic expertise. In this paper, we propose a Multi-Task Active Learning framework for Semantic Role Labeling with Entity Recognition (ER) as the auxiliary task to alleviate the need for extensive data and use additional information from ER to help SRL. We evaluate our approach on Indonesian conversational dataset. Our experiments show that multi-task active learning can outperform single-task active learning method and standard multi-task learning. According to our results, active learning is more efficient by using 12% less of training data compared to passive learning in both single-task and multi-task setting. We also introduce a new dataset for SRL in Indonesian conversational domain to encourage further research in this area.

本文提出了一种多任务主动学习的语义角色标注框架，以实体识别作为辅助任务，缓解了大量数据和语言专业知识的需求，并证明多任务主动学习可超越单任务主动学习和标准多任务学习方法，在Indonesian conversational dataset上得到验证。

多任务主动学习在低资源对话语料上的神经语义角色标注