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

Fariz Ikhwantri, Samuel Louvan, Kemal Kurniawan, Bagas Abisena, Valdi Rachman, Alfan Farizki Wicaksono, Rahmad Mahendra

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

11 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationACL 2018 - Deep Learning Approaches for Low-Resource Natural Language Processing, DeepLo 2018 - Proceedings of the Workshop
PublisherAssociation for Computational Linguistics (ACL)
Pages43-50
Number of pages8
ISBN (Electronic)9781948087476
Publication statusPublished - 2018
EventACL 2018 Workshop on Deep Learning Approaches for Low-Resource Natural Language Porcessing, DeepLo 2018 - Melbourne, Australia
Duration: 19 Jul 2018 → …

Publication series

NameProceedings of the Annual Meeting of the Association for Computational Linguistics
ISSN (Print)0736-587X

Conference

ConferenceACL 2018 Workshop on Deep Learning Approaches for Low-Resource Natural Language Porcessing, DeepLo 2018
Country/TerritoryAustralia
CityMelbourne
Period19/07/18 → …

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