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

WS 2018 Fariz IkhwantriSamuel LouvanKemal KurniawanBagas AbisenaValdi RachmanAlfan Farizki WicaksonoRahmad Mahendra

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... (read more)

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