SMUGRI - MT - Machine Translation System for Low-Resource F inno- U gric Languages (2024)

Abstract: We introduce SMUGRI-MT, an online neural machine translation system that covers 20 low-resource Finno-Ugric languages, along with seven high-resource languages.

## introduction this project focuses on neural machine translation (mt) for the finno-ugric languages. besides three mid-resource languages (estonian, finnish and hungarian), this family includes dozens more that range from low-resource (e.g. komi, veps) to extremely endangered and under-supported languages (e.g. livonian, votic). our goal is to include as many of these languages and varieties as possible and provide them with reliable mt models and methodology. our work on developing mt systems for lowresource finno-ugric languages started in 2021, initially focusing on võro as well as southern and northern sami @xcite . one year later, we added inari, skolt and lule sami languages @xcite and developed an mt system for livonian @xcite . last year, we significantly expanded the scope of our mt system to include a total of 20 low-resource © 2024 the authors. this article is licensed under a creative commons 4.0 licence, no derivative works, attribution, cc-by-nd. * equal contribution languages and dialects @xcite . since the last version, we have collected more data, transitioned to a different multilingual pretrained model @xcite and implemented language identification and hallucination detection tests to ensure a cleaner dataset. below we describe the current state of the developed mt system and outline the future challenges. ## the current stage the version currently available online foot_0 is tailored for 20 low-resource languages: mansi, khanty, komi, komi-permyak, udmurt, meadow and hill mari, north sami, south sami, inari sami, lule sami, skolt sami, erzya, moksha, ludian, proper karelian, võro, veps, livvi karelian, livonian along with seven high-resource languages: english, estonian, finnish, hungarian, latvian, norwegian (bokmål), and russian. benchmark test sets are currently available for only nine low-resource languages: komi, udmurt, hill and meadow mari, erzya, moksha, livonian, mansi, and livvi karelian @xcite . ## future challenges although this work started in 2021, the project smugri-mt has received funding only recently, thus there is still a lot of work to be done more systematically. our efforts will focus on three directions of future research: data: we will continue to collect parallel and monolingual data for currently supported languages, as well as expand our dataset to include new languages and varieties. in addition to data collection, we will prioritize preprocessing steps to produce cleaner corpora. several finno-ugric languages (already included and these to be added) do not have a normalized orthography and additionally represent a mixture of dialects and varieties. therefore an important future direction is separating the varieties from each other and deciding what to do about orthographic variation. analysis: we plan to conduct a detailed qualitative analysis of the translations to identify errors overlooked by automatic metrics. additionally, we aim to develop test sets across various domains and language varieties, enabling a more comprehensive qualitative and quantitative analysis. architecture: the current system translates one sentence at a time. we have plans to transition to paragraph-level and document-level systems to reduce at least gender-related issues. this is mainly due to the finno-ugric languages being gender-neutral, making it impossible to determine the correct gender without context and making it important to translate genders consistently throughout the document when generating a language with grammatical gender (like english).