Improving Low-resource Dependency Parsing Using Multi-strategy Data Augmentation

Abstract

Dependency parsing aims to identify syntactic dependencies between words in a sentence.Dependency parsing can provide syntactic features and improve model performance for tasks such as information extraction,automatic question answering and machine translation.The training data size has an significant impact on the performance of the dependency parsing model.The lack of training data will cause serious unknown word problems and model over-fitting problems.This paper proposes various data augment strategies for the problem of low-resource dependency parsing.The proposed method effectively expands the training data by synonym substitution and alleviates the unknown words problem.The data augment strategies of multiple Mixups effectively alleviate the model overfitting problem and improve the generalization ability of the model.Experimental results on the universal dependencies treebanks(UD treebanks) dataset show that the proposed methods effectively improve the performance of Thai,Vietnamese and English dependency parsing under small-scale training corpus conditions.

Keywords

• dependency parsing|low-resource language|mixup data augmentation|synonym substitution|multi-strategy