A kiba NLP - TUT : Injecting Language-Specific Word-Level Noise for Low-Resource Language Translation (2025)

Abstract: In this paper, we describes our system for the WMT 2025 Low-Resource Indic Language Translation Shared Task.The language directions addressed are Assamese↔English and Manipuri→English.We propose a method to improve translation performance from low-resource languages (LRLs) to English by injecting Language-specific word-level noise into the parallel corpus of a closely related high-resource language (HRL).In the proposed method, word replacements are performed based on edit distance, using vocabulary and frequency information extracted from an LRL monolingual corpus.Experiments conducted on Assamese and Manipuri show that, in the absence of LRL parallel data, the proposed method outperforms both the w/o noise setting and existing approaches. Furthermore, we confirmed that increasing the size of the monolingual corpus used for noise injection leads to improved translation performance.

## introduction there are approximately 7,000 languages in the world, but only a small subset of high-resource languages (hrls) have sufficiently developed parallel corpora for machine translation (mt). for these languages, research leveraging few-shot learning with parallel data @xcite and largescale multilingual language models (mllms) @xcite has progressed. such efforts have enabled the learning of shared cross-lingual embedding spaces, thereby facilitating cross-lingual transfer. in contrast, many languages are low-resource languages (lrls) for which only monolingual data is available. compared to parallel data, monolingual data is easier to collect and is widely used for techniques such as back-translation (bt) @xcite and continued pretraining of mllms. this study aims to improve @xmath0, word-level noise into parallel data of closely related hrls. @xcite proposed charspan, a method that injects character-level noise into hrl parallel data with high lexical similarity to a lrl. this method improves translation accuracy for the lrl by leveraging its character list. however, this approach does not sufficiently capture word-level statistical characteristics such as the lrl's vocabulary frequency and distribution. to address this limitation, we propose a method that uses word lists and frequency information extracted from lrl monolingual data to add wordlevel noise to hrl parallel data. we further analyze the effect of this method under conditions where lrl parallel data is available versus unavailable. experimental results show that when using only lrl monolingual data, our method outperforms existing approaches. in contrast, when lrl parallel data is available, the performance gap with existing methods is small. we also observe that increasing the size of the monolingual data used for noise injection tends to improve performance, and that including the test dataset in the monolingual data yields additional performance gains. ## related work some studies have examined the effects of adding noise to parallel data on its diversity and the ro-bustness of translation models, but the impact on cross-lingual transfer has not been thoroughly investigated. @xcite proposed word dropout, which randomly sets some word embeddings to zero vectors. @xcite proposed a data augmentation method that adds random word replacements to parallel data. while both of these methods enhance the diversity of parallel data, their effectiveness in improving crosslingual transfer capability is limited. a representative data augmentation technique that leverages monolingual data in neural machine translation is back-translation (bt) @xcite . when parallel data is scarce, bt generates pseudo-parallel data by using target-side monolingual data and a reverse-direction translation model. more recently, iterative back-translation (ibt) @xcite @xcite has been proposed, which extends bt in both directions. ibt utilizes monolingual data from both sides to generate pseudoparallel data in both directions and iteratively alternates between generating this data and updating the translation models in both directions. ## method in this chapter, we propose a method to enhance robustness in @xmath1. the noise consists solely of word replacements, where the edit distance is selected based on a geometric distribution. the replacement candidates are chosen using frequency-weighted selection, thereby injecting lrl words into the related hrl. specifically, we use approximately 160,000 sentences of assamese monolingual text to add noise to bengali-english parallel data. figure 1 shows an overview of the proposed method. an example of the noise injection process is illustrated in figure 2 . furthermore, by using the model trained on the noise-injected parallel data as a back-translation model, we perform @xmath2. ## conclusion in this study, we proposed language-specific wordlevel noise injection method for the parallel corpus of a hrl closely related to a lrl, using vocabulary and frequency information extracted from the lrl's monolingual data. experiments on assamese and manipuri demonstrated that, particularly in the absence of parallel data, the proposed method outperforms both the w/o noise setting and existing approaches. furthermore, we showed that increasing the size of the monolingual data contributes to improved translation performance.