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Register a new userAttentive fine-tuning of Transformers for Translation of low-resourced languages @LoResMT 2021
التهاب الدقيقة من محولات الترجمة لترجمة لغات منخفضة الموارد Lolorsmt 2021
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تقارير هذه الورقة أنظمة الترجمة الآلية المقدمة من فريق IIITT للغة الإنجليزية → أزواج اللغة المهاراتية والإنجليزية أزواج LORESMT 2021 المشاركة المشتركة.تركز المهمة على الحصول على ترجمات استثنائية لغات منخفضة بالموارد منخفضة إلى حد ما مثل الأيرلندية والماراثية.نحن نايت Tune Endertrans، نموذج NMT متعدد اللغات مسبقا للغة الإنجليزية → Marathi، باستخدام Corpus الموازي الخارجي كمدخل للتدريب الإضافي.لقد استخدمنا نموذج English Helsinki-NLP Opus Mt للزواج باللغة الأخير.تؤدي نهجنا إلى نتائج واعدة نسبيا على مقاييس بلو.تحت اسم الفريق IIITT، تصنيف أنظمتنا في المرتبة 1، 1، و 2 باللغة الإنجليزية → الماراثي، الأيرلندية → الإنجليزية، والإنجليزية → الأيرلندية على التوالي.يتم نشر رموز أنظمتنا 1.
This paper reports the Machine Translation (MT) systems submitted by the IIITT team for the English→Marathi and English⇔Irish language pairs LoResMT 2021 shared task. The task focuses on getting exceptional translations for rather low-resourced languages like Irish and Marathi. We fine-tune IndicTrans, a pretrained multilingual NMT model for English→Marathi, using external parallel corpus as input for additional training. We have used a pretrained Helsinki-NLP Opus MT English⇔Irish model for the latter language pair. Our approaches yield relatively promising results on the BLEU metrics. Under the team name IIITT, our systems ranked 1, 1, and 2 in English→Marathi, Irish→English, and English→Irish respectively. The codes for our systems are published1 .
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We present the findings of the LoResMT 2021 shared task which focuses on machine translation (MT) of COVID-19 data for both low-resource spoken and sign languages. The organization of this task was conducted as part of the fourth workshop on technolo
gies for machine translation of low resource languages (LoResMT). Parallel corpora is presented and publicly available which includes the following directions: English↔Irish, English↔Marathi, and Taiwanese Sign language↔Traditional Chinese. Training data consists of 8112, 20933 and 128608 segments, respectively. There are additional monolingual data sets for Marathi and English that consist of 21901 segments. The results presented here are based on entries from a total of eight teams. Three teams submitted systems for English↔Irish while five teams submitted systems for English↔Marathi. Unfortunately, there were no systems submissions for the Taiwanese Sign language↔Traditional Chinese task. Maximum system performance was computed using BLEU and follow as 36.0 for English--Irish, 34.6 for Irish--English, 24.2 for English--Marathi, and 31.3 for Marathi--English.
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Data-to-text (D2T) generation in the biomedical domain is a promising - yet mostly unexplored - field of research. Here, we apply neural models for D2T generation to a real-world dataset consisting of package leaflets of European medicines. We show t
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This paper describes the submission to the IWSLT 2021 Low-Resource Speech Translation Shared Task by IMS team. We utilize state-of-the-art models combined with several data augmentation, multi-task and transfer learning approaches for the automatic s
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