Robust Neural Machine Translation for Abugidas by Glyph Perturbation (2024)

Abstract: Neural machine translation (NMT) systems are vulnerable when trained on limited data. This is a common scenario in low-resource tasks in the real world. To increase robustness, a solution is to intently add realistic noise in the training phase. Noise simulation using text perturbation has been proven to be efficient in writing systems that use Latin letters. In this study, we further explore perturbation techniques on more complex abugida writing systems, for which the visual similarity of complex glyphs is considered to capture the essential nature of these writing systems. Besides the generated noise, we propose a training strategy to improve robustness. We conducted experiments on six languages: Bengali, Hindi, Myanmar, Khmer, Lao, and Thai. By overcoming the introduced noise, we obtained non-degenerate NMT systems with improved robustness for low-resource tasks for abugida glyphs.

## introduction neural machine translation (nmt) systems have been shown to be vulnerable in noisy settings, where slightly modified inputs cause serious translation failures @xcite . @xcite showed that techniques using pre-trained language models cannot prevent this. this drawback is more disastrous in low-resource scenarios, where the model's robustness becomes a crucial issue. several text perturbation techniques have been developed to improve robustness by introducing synthesized textual noise. typical techniques are deepwordbug @xcite , textbugger @xcite , and viper @xcite . these techniques mostly focus on languages that use alphabetic systems, such as latin letters. as a more complex writing system, chinese characters were investigated by @xcite ; @xcite . in the present study, we further fill the a reasonable perturbation technique should produce meaningful and readable text that is indistinguishable for humans, but disastrous for a system's prediction @xcite . visually similar glyphs or homoglyphs foot_0 were investigated in @xcite ; @xcite , and @xcite obtained realistic samples from large corpora. as a primary contribution, we further develop these previous studies for abugida writing systems. some exemplary homoglyphs in various abugida systems are illustrated in figure 1 . to address noise, we propose a training strategy that leverages adversarial training, subword regularization, and consistency training. we selected six languages that use abugida systems, bengali, hindi, myanmar, khmer, lao, and thai, and experimented on them for low-resource tasks. overcoming noisy perturbations improved the robustness of nmt systems, with non-degenerate performance. ## conclusion in this study, we presented a perturbation approach that leverages visual similarity and introduced a training strategy to maintain the performance of the nmt model. we exposed the vulnerability of the vanilla nmt model through experiments that perturbed test data using homoglyphs, and demonstrated the importance of robust training against text perturbation. the findings of this study can aid future research effort in evaluating the generalization capabilities of nmt models, particularly for low-resource settings and understudied languages.