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Register a new userNHK's Lexically-Constrained Neural Machine Translation at WAT 2021
NHK's Translation العصبية المقيدة من NHK في WAT 2021
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تصف هذه الورقة نظام فريقنا (NHK) لمهمة ترجمة الآلة اليابانية والإنجليزية اليابانية والإنجليزية.في هذه المهمة، الهدف هو تحسين الجودة مع الحفاظ على مصطلحات ثابتة للترجمة الورقية العلمية.هذه المهمة لها ميزة فريدة من نوعها، حيث يتم تقديم بعض الكلمات في جملة مستهدفة بالإضافة إلى جملة مصدر.في هذه الورقة، نستخدم ترجمة آلية عصبية مقيدة متعمدة (NMT)، والتي تسلحن جملة المصدر والكلمات المقيدة مع رمز خاص لإدخالها في تشفير NMT.مفتاح NMT الناجح المقيد متعمدا هو الطريق لاستخراج القيود من الجملة المستهدفة من بيانات التدريب.نقترح طريقين استخراجي: القيد الأساسي الصحيح وعيد الكلمة.هاتان الطريقتين يعتبران أهمية الكلمات والخلط في NMT، على التوالي.تظهر نتائج التقييم فعالية طريقة القيد المعجمية لدينا.
This paper describes the system of our team (NHK) for the WAT 2021 Japanese-English restricted machine translation task. In this task, the aim is to improve quality while maintaining consistent terminology for scientific paper translation. This task has a unique feature, where some words in a target sentence are given in addition to a source sentence. In this paper, we use a lexically-constrained neural machine translation (NMT), which concatenates the source sentence and constrained words with a special token to input them into the encoder of NMT. The key to the successful lexically-constrained NMT is the way to extract constraints from a target sentence of training data. We propose two extraction methods: proper-noun constraint and mistranslated-word constraint. These two methods consider the importance of words and fallibility of NMT, respectively. The evaluation results demonstrate the effectiveness of our lexical-constraint method.
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This paper describes ANVITA-1.0 MT system, architected for submission to WAT2021 MultiIndicMT shared task by mcairt team, where the team participated in 20 translation directions: English→Indic and Indic→English; Indic set comprised of 10 Indian lang
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