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Register a new userRule-based Morphological Inflection Improves Neural Terminology Translation
انعطاف مورفولوجي يستند إلى القواعد يحسن ترجمة المصطلحات العصبية
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تتحمل الأساليب الحالية لإدماج قيود المصطلحات في الترجمة الآلية (MT) عادة أن شرط القيد يتم توفيرها في أشكالهم المورفولوجية الصحيحة. هذا يحد من تطبيقه إلى سيناريوهات العالم الحقيقي حيث يتم توفير شروط القيد كمولماس. في هذه الورقة، نقدم إطارا وحدات لإدماج قيود Lemma في MT العصبية (NMT) التي يمكن فيها تطبيق المعرفة اللغوية وأنواع متنوعة من نماذج NMT بشكل مرني. يعتمد ذلك على وحدة انعطاف عبر اللغات الرواية التي تلحق قيود LEMMA المستهدفة بناء على سياق المصدر. نستكشف وحدات الانقسام العصبية المستندة إلى القواعد ذات الدوافع التي تعتمد على القواعد ومقرها إلى البيانات وتصميم أجنحة اختبار الصحة باللغة الإنجليزية والألمانية والأخبار الإنجليزية - الليتوانية لتقييمها في تكييف المجال وإعدادات MT منخفضة الموارد. تشير النتائج إلى أن وحدة الانعكاسات التي تعتمد على قواعدنا تساعد نماذج NMT على دمج قيود LEMMA بشكل أكثر دقة من الوحدة العصبية وتتفوق على النهج الحالي للنهائي مع انخفاض تكاليف التدريب.
Current approaches to incorporating terminology constraints in machine translation (MT) typically assume that the constraint terms are provided in their correct morphological forms. This limits their application to real-world scenarios where constraint terms are provided as lemmas. In this paper, we introduce a modular framework for incorporating lemma constraints in neural MT (NMT) in which linguistic knowledge and diverse types of NMT models can be flexibly applied. It is based on a novel cross-lingual inflection module that inflects the target lemma constraints based on the source context. We explore linguistically motivated rule-based and data-driven neural-based inflection modules and design English-German health and English-Lithuanian news test suites to evaluate them in domain adaptation and low-resource MT settings. Results show that our rule-based inflection module helps NMT models incorporate lemma constraints more accurately than a neural module and outperforms the existing end-to-end approach with lower training costs.
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