@inproceedings{bjerva-etal-2019-transductive,
title = "Transductive Auxiliary Task Self-Training for Neural Multi-Task Models",
author = "Bjerva, Johannes and
Kann, Katharina and
Augenstein, Isabelle",
editor = "Cherry, Colin and
Durrett, Greg and
Foster, George and
Haffari, Reza and
Khadivi, Shahram and
Peng, Nanyun and
Ren, Xiang and
Swayamdipta, Swabha",
booktitle = "Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-6128",
doi = "10.18653/v1/D19-6128",
pages = "253--258",
abstract = "Multi-task learning and self-training are two common ways to improve a machine learning model{'}s performance in settings with limited training data. Drawing heavily on ideas from those two approaches, we suggest transductive auxiliary task self-training: training a multi-task model on (i) a combination of main and auxiliary task training data, and (ii) test instances with auxiliary task labels which a single-task version of the model has previously generated. We perform extensive experiments on 86 combinations of languages and tasks. Our results are that, on average, transductive auxiliary task self-training improves absolute accuracy by up to 9.56{\%} over the pure multi-task model for dependency relation tagging and by up to 13.03{\%} for semantic tagging.",
}
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<abstract>Multi-task learning and self-training are two common ways to improve a machine learning model’s performance in settings with limited training data. Drawing heavily on ideas from those two approaches, we suggest transductive auxiliary task self-training: training a multi-task model on (i) a combination of main and auxiliary task training data, and (ii) test instances with auxiliary task labels which a single-task version of the model has previously generated. We perform extensive experiments on 86 combinations of languages and tasks. Our results are that, on average, transductive auxiliary task self-training improves absolute accuracy by up to 9.56% over the pure multi-task model for dependency relation tagging and by up to 13.03% for semantic tagging.</abstract>
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%0 Conference Proceedings
%T Transductive Auxiliary Task Self-Training for Neural Multi-Task Models
%A Bjerva, Johannes
%A Kann, Katharina
%A Augenstein, Isabelle
%Y Cherry, Colin
%Y Durrett, Greg
%Y Foster, George
%Y Haffari, Reza
%Y Khadivi, Shahram
%Y Peng, Nanyun
%Y Ren, Xiang
%Y Swayamdipta, Swabha
%S Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F bjerva-etal-2019-transductive
%X Multi-task learning and self-training are two common ways to improve a machine learning model’s performance in settings with limited training data. Drawing heavily on ideas from those two approaches, we suggest transductive auxiliary task self-training: training a multi-task model on (i) a combination of main and auxiliary task training data, and (ii) test instances with auxiliary task labels which a single-task version of the model has previously generated. We perform extensive experiments on 86 combinations of languages and tasks. Our results are that, on average, transductive auxiliary task self-training improves absolute accuracy by up to 9.56% over the pure multi-task model for dependency relation tagging and by up to 13.03% for semantic tagging.
%R 10.18653/v1/D19-6128
%U https://aclanthology.org/D19-6128
%U https://doi.org/10.18653/v1/D19-6128
%P 253-258
Markdown (Informal)
[Transductive Auxiliary Task Self-Training for Neural Multi-Task Models](https://aclanthology.org/D19-6128) (Bjerva et al., 2019)
ACL