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Register a new userImproving Machine Translation of Rare and Unseen Word Senses
تحسين الترجمة آلة حواس الكلمة النادرة وغير المرئية
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تحسنت أداء أنظمة NMT بشكل كبير في السنوات القليلة الماضية ولكن ترجمة الكلمات متعددة الإحساس لا تزال تشكل تحديا. نظرا لأن حواس الكلمات ليست ممثلة بشكل موحد في الشركة الموازية المستخدمة للتدريب، فهناك استخدام مفرط من المعنى الأكثر شيوعا في إخراج MT. في هذا العمل، نقترح CMBT (الترجمة ذات الاحتياط بالسياقة)، وهو نهج لتحسين ترجمة كلمة متعددة الشعور بالاستفادة من تمثيل الكلمات السياقية المتبادلة المدربة مسبقا (CCWRS). بسبب حساسية السياق الخاصة بهم وبياناتها السابقة للتدريب الكبيرة، يمكن ل CCWRS الوصول بسهولة إلى حواس الكلمات المفقودة أو نادرة جدا في ولاية فورانيا المستخدمة لتدريب MT. على وجه التحديد، تطبق CMBT تحريض معجم ثنائي اللغة على CCWRS الجمل المستهدفة ذات المحور المنطقي من مجموعة بيانات أحادية الأحادية، ثم يترجم هذه الجمل لإنشاء كورب موازية زائفة كبيانات تدريبية إضافية لنظام MT. نحن نختبر جودة الترجمة من الكلمات الغامضة على جناح اختبار المخاط المخاطي، والتي تم بناؤها لاختبار فعالية حذف كلمة معنى الكلمة لأنظمة MT. نظهر أن نظامنا يتحسن على ترجمة حواس كلمة متعددة الترددات الصعبة والثانية.
The performance of NMT systems has improved drastically in the past few years but the translation of multi-sense words still poses a challenge. Since word senses are not represented uniformly in the parallel corpora used for training, there is an excessive use of the most frequent sense in MT output. In this work, we propose CmBT (Contextually-mined Back-Translation), an approach for improving multi-sense word translation leveraging pre-trained cross-lingual contextual word representations (CCWRs). Because of their contextual sensitivity and their large pre-training data, CCWRs can easily capture word senses that are missing or very rare in parallel corpora used to train MT. Specifically, CmBT applies bilingual lexicon induction on CCWRs to mine sense-specific target sentences from a monolingual dataset, and then back-translates these sentences to generate a pseudo parallel corpus as additional training data for an MT system. We test the translation quality of ambiguous words on the MuCoW test suite, which was built to test the word sense disambiguation effectiveness of MT systems. We show that our system improves on the translation of difficult unseen and low frequency word senses.
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