Data Augmentation Based Event Detection

TP3-05; Supervised models for event detection usually require large-scale human-annotated training data, especially neural models. A data augmentation technique is proposed to improve the performance of event detection by generating paraphrase sentences to enrich expressions of the original data. Sp...

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Bibliographic Details
Published in东华大学学报（英文版） Vol. 38; no. 6; pp. 511 - 518
Main Authors DING Xiangwu, DING Jingjing, QIN Yanxia
Format Journal Article
LanguageEnglish
Published College of Computer Science and Technology,Donghua University,Shanghai 201620,China 31.12.2021
Subjects
multi-instance learning ( MIL)
annotation alignment algorithm
back translation
data augmentation
event detection
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ISSN1672-5220
DOI10.19884/j.1672-5220.202105009

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Summary:TP3-05; Supervised models for event detection usually require large-scale human-annotated training data, especially neural models. A data augmentation technique is proposed to improve the performance of event detection by generating paraphrase sentences to enrich expressions of the original data. Specifically, based on an existing human-annotated event detection dataset, we first automatically build a paraphrase dataset and label it with a designed event annotation alignment algorithm. To alleviate possible wrong labels in the generated paraphrase dataset, a multi-instance learning ( MIL ) method is adopted for joint training on both the gold human-annotated data and the generated paraphrase dataset. Experimental results on a widely used dataset ACE2005 show the effectiveness of our approach.
ISSN:1672-5220
DOI:10.19884/j.1672-5220.202105009