Data Augmentation by Concatenation for Low-Resource Translation: A\n Mystery and a Solution

2021/05/04 by Toan Quoc Nguyen, Kenton Murray, Nguyen, Toan Q. +3
Computer Science · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Natural Language Processing Techniques #Speech Recognition and Synthesis #Topic Modeling

paper · pdf · doi:10.48550/arxiv.2105.01691

openalex publication_date 2021/05/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

In this paper, we investigate the driving factors behind concatenation, a\nsimple but effective data augmentation method for low-resource neural machine\ntranslation. Our experiments suggest that discourse context is unlikely the\ncause for the improvement of about +1 BLEU across four language pairs. Instead,\nwe demonstrate that the improvement comes from three other factors unrelated to\ndiscourse: context diversity, length diversity, and (to a lesser extent)\nposition shifting.\n

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