Data Augmentation for Machine Translation via Dependency Subtree Swapping

TL;DR

This paper introduces a novel data augmentation method for machine translation that swaps dependency subtrees between source and target sentences to improve translation quality.

Contribution

It proposes a generic dependency subtree swapping framework with filtering heuristics, enhancing data diversity for machine translation models.

Findings

Consistent BLEU score improvements in 3 out of 4 language pairs

Effective augmentation method applicable to multiple language pairs

Open-source code available for reproducibility

Abstract

We present a generic framework for data augmentation via dependency subtree swapping that is applicable to machine translation. We extract corresponding subtrees from the dependency parse trees of the source and target sentences and swap these across bisentences to create augmented samples. We perform thorough filtering based on graphbased similarities of the dependency trees and additional heuristics to ensure that extracted subtrees correspond to the same meaning. We conduct resource-constrained experiments on 4 language pairs in both directions using the IWSLT text translation datasets and the Hunglish2 corpus. The results demonstrate consistent improvements in BLEU score over our baseline models in 3 out of 4 language pairs. Our code is available on GitHub.