Low-Resource Interlinear Translation: Morphology-Enhanced Neural Models for A ncient G reek (2025)

Abstract: Contemporary machine translation systems prioritize fluent, natural-sounding output with flexible word ordering. In contrast, interlinear translation maintains the source text’s syntactic structure by aligning target language words directly beneath their source counterparts. Despite its importance in classical scholarship, automated approaches to interlinear translation remain understudied. We evaluated neural interlinear translation from Ancient Greek to English and Polish using four transformer-based models: two Ancient Greek-specialized (GreTa and PhilTa) and two general-purpose multilingual models (mT5-base and mT5-large). Our approach introduces novel morphological embedding layers and evaluates text preprocessing and tag set selection across 144 experimental configurations using a word-aligned parallel corpus of the Greek New Testament. Results show that morphological features through dedicated embedding layers significantly enhance translation quality, improving BLEU scores by 35% (44.67 → 60.40) for English and 38% (42.92 → 59.33) for Polish compared to baseline models. PhilTa achieves state-of-the-art performance for English, while mT5-large does so for Polish. Notably, PhilTa maintains stable performance using only 10% of training data. Our findings challenge the assumption that modern neural architectures cannot benefit from explicit morphological annotations. While preprocessing strategies and tag set selection show minimal impact, the substantial gains from morphological embeddings demonstrate their value in low-resource scenarios.

## introduction machine translation (mt) is a well-established subfield in natural language processing (nlp), primarily focused on producing accurate and natural translations. in typical scenarios, mt systems have the flexibility to reorder words or go beyond literal meanings to account for syntactic differences between source and target languages. while these conventional mt systems prioritize natural and fluent translations, there exists a spectrum of translation approaches, ranging from free translation to extremely literal renderings. at the far end of this spectrum lies interlinear translation @xcite , a method that strictly preserves the source text's syntactic structure. this approach aligns target language words directly below or above their corresponding source text elements. commonly applied to ancient (and oftentimes sacred) texts, this method allows readers unfamiliar with the source language to understand both the meaning and structure of the original text. such alignment enables students to critically evaluate translations by observing how specific source words were translated, which is especially crucial for interpreting source texts in fields such as philosophy and religious studies. figure 1 illustrates an example of interlinear translation. despite the significance of interlinear translation, which @xcite called "the archetype or ideal of all translation", there has been limited research on automating this process. this may be attributed to the pre-existing interlinear translations for many influential texts. however, we believe automating this process remains relevant, making these texts more accessible to those without expertise in ancient languages. while prior research @xcite suggests that modern neural architectures like bert inherently learn linguistic patterns without explicit ? ??? ?? ? ??? ??? ????????? ??? ??? ? ??? ? ???? arise take up the mat of you and walk linguistic annotations, our findings challenge this assumption in low-resource scenarios. we demonstrate that for small datasets with limited sentence pairs, properly encoded morphosyntactic tags significantly enhance translation performance. ## related work recent years have witnessed substantial advances in applying machine learning to ancient languages, particularly ancient greek @xcite . while most research focuses on tasks like pos tagging and lemmatization, machine translation of ancient texts presents unique challenges that intersect multiple research areas. this section examines relevant work across these domains. ## methodology in this section we discuss our corpora, including gathering, alignment, and preprocessing of the data. further, we cover models employed and our approaches for encoding the morphological metadata in their inputs. finally, we describe how the models were fine-tuned. ## evaluation we evaluate model performance using bleu @xcite and semscore @xcite backed by all-mpnet-base-v2 foot_2 . while modern metrics like comet @xcite could provide better assessment, they lack ancient greek support, so we could not apply them in these experiments. to ensure fair evaluation, separator tokens are removed from the output sequences before comparison with references, preventing the metrics from artificially rewarding proper output formatting. statistical significance of differences between configurations was assessed using two-sided mann-whitney u tests @xcite . ## results we address each research question in the subsequent sections, beginning with an examination of the overall performance of the models. we then compare the performance of each base model used for fine-tuning. finally, we investigate the impact of morphological metadata and text preprocessing on the final results. all scores presented in this section represent the bleu score obtained on the test split. ## conclusions we demonstrated the feasibility of automated interlinear translation from ancient greek, achieving bleu scores above 60 and semscore values exceeding 0.8 for both target languages. philta outperformed larger models for english (60.40 bleu), while mt5-large performed best for polish (59.33 bleu). our novel morphological information encoding through dedicated embedding layers substantially improved translation quality, with gains of 38% for polish (59.33 vs 42.92 bleu) and 35% for english (60.40 vs 44.67 bleu) over the baseline. philta showed remarkable stability in lowresource scenarios, maintaining consistent performance (bleu 36.20-43.52) with just 10% of the dataset, while mt5-large struggled with smaller samples. this challenges the assumption that exposure to multiple languages necessarily provides an advantage in adaptation. the interlinear translations' strict syntax enabled cross-language comparisons, revealing different metric correlations (bleu-semscore: @xmath0.97 english, @xmath1.89 polish). while models trained on text with preserved diacritics achieved numerically better results, these differences were not statistically significant. similarly, the choice between morphological tag sets showed minimal impact across both target languages. future work could explore targeted polish pretraining, given philta's english success.