Data augmentation for low‐resource languages NMT guided by constrained sampling. Issue 1 (25th August 2021)

• Record Type: Journal Article
• Authors: Maimaiti, Mieradilijiang; Liu, Yang; Luan, Huanbo; Sun, Maosong
• Is Part Of: International journal of intelligent systems. Volume 37:Issue 1(2022)
• Page Start: 30
• Page End: 51
• Publication Date: 2021-08-25
• Subjects: artificial intelligence -- constrained sampling -- data augmentation -- low‐resource languages -- natural language processing -- neural machine translation; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; 006.3
• DOI: 10.1002/int.22616
• Languages: English
• ISSNs: 0884-8173

Abstract

Abstract: Data augmentation (DA) is a ubiquitous approach for several text generation tasks. Intuitively, in the machine translation paradigm, especially in low‐resource languages scenario, many DA methods have appeared. The most commonly used methods are building pseudocorpus by randomly sampling, omitting, or replacing some words in the text. However, previous approaches hardly guarantee the quality of augmented data. In this study, we try to augment the corpus by introducing a constrained sampling method. Additionally, we also build the evaluation framework to select higher quality data after augmentation. Namely, we use the discriminator submodel to mitigate syntactic and semantic errors to some extent. Experimental results show that our augmentation method consistently outperforms all the previous state‐of‐the‐art methods on both small and large‐scale corpora in eight language pairs from four corpora by 2.38–4.18 bilingual evaluation understudy points.