Training an NMT system for legal texts of a low-resource language variety South Tyrolean G erman - I talian (2024)

Abstract: This paper illustrates the process of training and evaluating NMT systems for a language pair that includes a low-resource language variety.A parallel corpus of legal texts for Italian and South Tyrolean German has been compiled, with South Tyrolean German being the low-resourced language variety. As the size of the compiled corpus is insufficient for the training, we have combined the corpus with several parallel corpora using data weighting at sentence level. We then performed an evaluation of each combination and of two popular commercial systems.

## introduction neural machine translation (nmt) has shown outstanding performance and translation quality compared to previous models @xcite . however, there is a translation quality gap to fill for low-resource languages @xcite @xcite due to the significant amount of parallel data that is required to learn useful mappings between languages @xcite . besides, the legal domain poses an increased challenge for nmt @xcite given the intricate nature of legal language, the necessity to use precise terminology, and the negative consequences of misunderstanding the legal intent @xcite . our work deals both with the legal domain and with a low-resource language variety of a big european language (german), as we translated decrees in the language combination italian -south tyrolean german. it shows some of the challenges of nmt in the legal domain, focusing on a low-resource variety. this lowresource variety context is not unique but shared by many language communities in relation to the legal and administrative domain; in europe, for example, by the german-speaking community in belgium, the swedish-speaking community in finland, the italian-speaking community in croatia, the danish-speaking community in germany and many more. after having trained our nmt systems with the lexb @xcite corpus by eurac research and having cleaned and curated the data afterwards, we achieved an increase of, depending on the direction of translation, approximately five to eight full points in bleu @xcite . this shows the importance of training domain-specific nmt systems with high-quality data, especially for low-resource languages. ## conclusions and future work low-resource language situations are challenging for nmt engines. we are working with a lowresource language variety of a major european language and with legal texts, which in itself is a lowresource situation and additionally requires a very particular language. we have trained an nmt model for the legal domain with the language combination italian -south tyrolean german, a low-resource language variety. to this end, we have used and processed a relevant available corpus of legal texts. as this in-domain corpus is not big enough to train nmt systems, we have augmented this data with combinations of other corpora: a corpus created from several eu corpora and multiparacrawl. the combinations are based on weighting at sentence level, giving higher weight to segments from the compiled in-domain corpus. as a baseline system, we have trained an nmt system using the multiparacrawl corpus only. results show that the best system is the one trained with the in-domain corpus combined with the eu corpora, as it performs better than commercial products for these language combinations. an evaluation was carried out using three of the most frequent assessment metrics (bleu, chrf2, ter). as positive as these results may seem, a qualitative breakdown of the results, with manual annotations along established criteria @xcite to better understand the specifics of the particular circumstances, is still pending and is planned as the next step. this paper shows that training tailored nmt systems can be a viable alternative to commercial systems in a low-resource scenario. even with limited in-domain data, using data from a similar domain and data weighting techniques, the final system can outperform widely used commercial systems. in particular, low-resource varieties of bigger languages tend to be neglected in research and nmt development, even though the consequences of mistranslation may be serious. with its systembound terminology and phraseology, the legal domain needs particular attention, as it is relevant for legal and translation professionals increasingly using nmt systems and the general public. finally, our results emphasize the importance of curated in-domain corpora to align the results of nmt models with those pertaining to situations with more data.