When Scripts Diverge: Strengthening Low-Resource Neural Machine Translation Through Phonetic Cross-Lingual Transfer (2025)

Abstract: Multilingual Neural Machine Translation (MNMT) models enhance translation quality for low-resource languages by exploiting cross-lingual similarities during training—a process known as knowledge transfer. This transfer is particularly effective between languages that share lexical or structural features, often enabled by a common orthography. However, languages with strong phonetic and lexical similarities but distinct writing systems experience limited benefits, as the absence of a shared orthography hinders knowledge transfer. To address this limitation, we propose an approach based on phonetic information that enhances token-level alignment across scripts by leveraging transliterations. We systematically evaluate several phonetic transcription techniques and strategies for incorporating phonetic information into NMT models. Our results show that using a shared encoder to process orthographic and phonetic inputs separately consistently yields the best performance for Khmer, Thai, and Lao in both directions with English, and that our custom Cognate-Aware Transliteration (CAT) method consistently improves translation quality over the baseline.

## introduction a common approach to enhancing neural machine translation (nmt) for low-resource languages involves leveraging the knowledge from similar high-resource languages. one approach to this is multilingual learning, in which a highresource language pair is combined with a lowresource language pair within a single multilingual model @xcite this method is effective with large models that support dozens of languages @xcite . the method also performs well on a smaller scale when pairing related languages, such as low-resource haitian creole with high-resource french @xcite , vietnamese and french @xcite , or catalan and several higher-resource indo-european languages @xcite . in these cases, the low-resource language improvements are enabled by the token overlap with the higherresource languages @xcite . this token overlap relies on the shared scripts between the high-and low-resource languages, a benefit not all low-resource languages have @xcite . some low-resource languages have a related high-resource counterpart but use a different writing system. despite strong phonetic and lexical similarities, the lack of a shared writing system almost completely eliminates token overlap, potentially limiting the benefits of transfer learning. one way to address this problem is by increasing token overlap; for example, @xcite achieve this by modifying the tokenizer, though our approach differs. in this work, we propose and evaluate a method for increasing token overlap in nmt models through the use of phonetic transliterations. specifically, we incorporate both phonetic information and the original orthographic representations of three southeast asian languages into a multilingual nmt (mnmt) model. our evaluation focuses on thai, lao, and khmer-closely related languages spoken in thailand, laos, and cambodia, respectively. although these languages share many lexical and grammatical similarities, each employs a distinct orthographic system. we compare a baseline multilingual nmt (mnmt) system, which uses only the orthographic representations of the languages, against three transliteration methods. the transliteration methods include international phonetic alphabet (ipa) transcriptions, romanization, and a custom method we call cognate-aware transliteration (cat). these transcriptions are integrated with the original orthographies in three ways: 1) by concatenating the orthographic and transliterated representations as a single input to a vanilla transformer, 2) by using a single encoder that processes the two inputs separately before concatenating their embeddings for a shared decoder, and 3) by using two separate encoders-one for the orthographic input and one for the transliterated inputcombined with a shared decoder. more details can be found in section 3. incorporating phonetic information allows mnmt models to overcome divergent orthographies and improve knowledge transfer between languages, boosting translation quality by up to 3.4 bleu points and 4.4 chrf points for lowresource southeast asian languages. additional results show that ipa and cat generally outperform romanization, with shared-encoder models achieving the largest gains over the baseline. overall, we contribute: ## related works previous research has been conducted for the cross-lingual transfer of various nlp tasks in chinese, japanese, korean, and vietnamese (cjkv). romanization has been used to enhance knowledge transfer in multilingual nmt models. a universal parent model trained with a romanized vocabulary was found to achieve improved knowledge transfer in a many-to-one translation scenario @xcite . amrhein and sennrich (2020) extended this approach to many-to-many nmt models and found that while romanization does not consistently improve results across all languages, it is beneficial in cases where related languages use different scripts. in such scenarios, romanization facilitates knowledge transfer. additionally, @xcite address this problem by abstracting vocabularies entirely. they utilize multilingual pixel representations, enabling the model to generalize to new and even unseen scripts as inputs. while prior work has applied romanization and phonetic representations to well-resourced language families, our study focuses on lowerresource southeast asian languages with limited transliteration tools in the underexplored domain of neural machine translation. ## methodology in section 3.1, we describe the non-transliterated baseline inputs and the three transliteration methods we intend to compare. in section 3.2 we describe the methods for computing token overlap between transliterated texts. finally, section 3.3 describes the methods for integrating the phonetic transcriptions into nmt models. ## conclusion low-resource languages with unique writing systems pose challenges for traditional neural machine translation (nmt) knowledge transfer techniques. in this work, we proposed methods for integrating phonetic transliterations to address the lack of shared orthographies between related high-and low-resource languages in multilingual nmt (mnmt) systems. specifically, we compared three transliteration schemes-international phonetic alphabet (ipa), romanization, and our custom cognate-aware transliterations (cat)together with three integration methods in a transformer model: concatenating inputs, using a shared encoder, and using dual encoders. we evaluated this methodology for khmer, lao, and thai in both directions with english, leveraging knowledge transfer from the higher-resource thai to the lower-resource lao and khmer. overall, integrating any transliteration method via any integration strategy improves translation performance in the @xmath0, while this approach can be extended to other language groups that share linguistic features but not orthography, such as maltese (latin script) and tunisian arabic (arabic script), with the potential to enhance translation for lower-resource languages. future work could also explore additional transliteration and integration methods, as well as leverage larger datasets such as opus for south-east asian languages, which would likely further improve performance above the baseline. beyond multilingual learning for knowledge transfer, additional work could explore whether integrating transliterations benefits parent-child finetuning @xcite in which a parent model is first trained on a highresource language pair and then fine-tuned on the low-resource language pair.