Dialectal and Low-Resource Machine Translation for Aromanian
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866909450310254592 |
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| author | Jerpelea, Alexandru-Iulius Rădoi, Alina Nisioi, Sergiu |
| author_facet | Jerpelea, Alexandru-Iulius Rădoi, Alina Nisioi, Sergiu |
| contents | This paper presents the process of building a neural machine translation system with support for English, Romanian, and Aromanian - an endangered Eastern Romance language. The primary contribution of this research is twofold: (1) the creation of the most extensive Aromanian-Romanian parallel corpus to date, consisting of 79,000 sentence pairs, and (2) the development and comparative analysis of several machine translation models optimized for Aromanian. To accomplish this, we introduce a suite of auxiliary tools, including a language-agnostic sentence embedding model for text mining and automated evaluation, complemented by a diacritics conversion system for different writing standards. This research brings contributions to both computational linguistics and language preservation efforts by establishing essential resources for a historically under-resourced language. All datasets, trained models, and associated tools are public: https://huggingface.co/aronlp and https://arotranslate.com |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_17728 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Dialectal and Low-Resource Machine Translation for Aromanian Jerpelea, Alexandru-Iulius Rădoi, Alina Nisioi, Sergiu Computation and Language This paper presents the process of building a neural machine translation system with support for English, Romanian, and Aromanian - an endangered Eastern Romance language. The primary contribution of this research is twofold: (1) the creation of the most extensive Aromanian-Romanian parallel corpus to date, consisting of 79,000 sentence pairs, and (2) the development and comparative analysis of several machine translation models optimized for Aromanian. To accomplish this, we introduce a suite of auxiliary tools, including a language-agnostic sentence embedding model for text mining and automated evaluation, complemented by a diacritics conversion system for different writing standards. This research brings contributions to both computational linguistics and language preservation efforts by establishing essential resources for a historically under-resourced language. All datasets, trained models, and associated tools are public: https://huggingface.co/aronlp and https://arotranslate.com |
| title | Dialectal and Low-Resource Machine Translation for Aromanian |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2410.17728 |