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Register a new userMachine Translation in the Covid domain: an English-Irish case study for LoResMT 2021
ترجمة آلية في مجال Covid: دراسة حالة باللغة الإنجليزية الأيرلندية ل Loresmt 2021
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تم تطوير نماذج الترجمة للمجال المحدد لترجمة بيانات CovID من الإنجليزية إلى الأيرلندية لمهمة LORESMT 2021 المشتركة.تم تطبيق تقنيات التكيف عن المجال، باستخدام كوربوس 55K 55K تكييفها كوفي من المديرية العامة للترجمة.تم مقارنة أداء الدقيقة والضبط الجمنيات المختلطة ومقارنة أساليب البيانات المشتركة مع النماذج المدربة على مجموعة بيانات داخلية ممتدة.كجزء من هذه الدراسة، تم تطوير مجموعة بيانات باللغة الإنجليزية والأيرلندية من البيانات ذات الصلة بالكوفت، من المجالات الصحية والتعليمية.يستخدم نموذج أعلى مستوياته بنية محول مدربة مع مجموعة بيانات Covid داخل المجال.في سياق هذه الدراسة، أظهرنا أن تمديد مجموعة بيانات أساسية 8K داخل المجال من خلال خطوط 5K فقط تحسنت درجة بلو بمقدار 27 نقطة.
Translation models for the specific domain of translating Covid data from English to Irish were developed for the LoResMT 2021 shared task. Domain adaptation techniques, using a Covid-adapted generic 55k corpus from the Directorate General of Translation, were applied. Fine-tuning, mixed fine-tuning and combined dataset approaches were compared with models trained on an extended in-domain dataset. As part of this study, an English-Irish dataset of Covid related data, from the Health and Education domains, was developed. The highestperforming model used a Transformer architecture trained with an extended in-domain Covid dataset. In the context of this study, we have demonstrated that extending an 8k in-domain baseline dataset by just 5k lines improved the BLEU score by 27 points.
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