DAGA: Data Augmentation with a Generation Approach for Low-resource Tagging Tasks

Data augmentation techniques have been widely used to improve machine learning performance as they enhance the generalization capability of models. In this work, to generate high quality synthetic data for low-resource tagging tasks, we propose a novel augmentation method with language models traine...

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Bibliographic Details
Published inarXiv.org
Main Authors Ding, Bosheng, Liu, Linlin, Bing, Lidong, Kruengkrai, Canasai, Nguyen, Thien Hai, Joty, Shafiq, Luo Si, Miao, Chunyan
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 03.11.2020
Subjects
Data augmentation
Data mining
Knowledge bases (artificial intelligence)
Machine learning
Marking
Sentences
Speech recognition
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Summary:Data augmentation techniques have been widely used to improve machine learning performance as they enhance the generalization capability of models. In this work, to generate high quality synthetic data for low-resource tagging tasks, we propose a novel augmentation method with language models trained on the linearized labeled sentences. Our method is applicable to both supervised and semi-supervised settings. For the supervised settings, we conduct extensive experiments on named entity recognition (NER), part of speech (POS) tagging and end-to-end target based sentiment analysis (E2E-TBSA) tasks. For the semi-supervised settings, we evaluate our method on the NER task under the conditions of given unlabeled data only and unlabeled data plus a knowledge base. The results show that our method can consistently outperform the baselines, particularly when the given gold training data are less.
ISSN:2331-8422