@inproceedings{ikhwantri-etal-2018-multi,
title = "Multi-Task Active Learning for Neural Semantic Role Labeling on Low Resource Conversational Corpus",
author = "Ikhwantri, Fariz and
Louvan, Samuel and
Kurniawan, Kemal and
Abisena, Bagas and
Rachman, Valdi and
Wicaksono, Alfan Farizki and
Mahendra, Rahmad",
editor = "Haffari, Reza and
Cherry, Colin and
Foster, George and
Khadivi, Shahram and
Salehi, Bahar",
booktitle = "Proceedings of the Workshop on Deep Learning Approaches for Low-Resource {NLP}",
month = jul,
year = "2018",
address = "Melbourne",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-3406/",
doi = "10.18653/v1/W18-3406",
pages = "43--50",
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."
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%0 Conference Proceedings
%T Multi-Task Active Learning for Neural Semantic Role Labeling on Low Resource Conversational Corpus
%A Ikhwantri, Fariz
%A Louvan, Samuel
%A Kurniawan, Kemal
%A Abisena, Bagas
%A Rachman, Valdi
%A Wicaksono, Alfan Farizki
%A Mahendra, Rahmad
%Y Haffari, Reza
%Y Cherry, Colin
%Y Foster, George
%Y Khadivi, Shahram
%Y Salehi, Bahar
%S Proceedings of the Workshop on Deep Learning Approaches for Low-Resource NLP
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne
%F ikhwantri-etal-2018-multi
%X 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.
%R 10.18653/v1/W18-3406
%U https://aclanthology.org/W18-3406/
%U https://doi.org/10.18653/v1/W18-3406
%P 43-50
Markdown (Informal)
[Multi-Task Active Learning for Neural Semantic Role Labeling on Low Resource Conversational Corpus](https://aclanthology.org/W18-3406/) (Ikhwantri et al., ACL 2018)
ACL