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Register a new userRevisiting Multi-Domain Machine Translation
إعادة النظر في ترجمة آلة متعددة المجالات
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عند بناء أنظمة الترجمة الآلات، يحتاج المرء في كثير من الأحيان إلى الاستفادة القصوى من مجموعات غير متجانسة من البيانات الموازية في التدريب، والتعامل مع المدخلات بقوة من المجالات غير المتوقعة في الاختبار.جذبت هذا السيناريو متعدد المجالات الكثير من العمل الحديث الذي يقع تحت المظلة العامة لتعلم النقل.في هذه الدراسة، نشجع الترجمة متعددة المجالات، بهدف صياغة الدوافع لتطوير هذه الأنظمة والتوقعات المرتبطة فيما يتعلق بالأداء.تبين تجاربنا مع عينة كبيرة من أنظمة المجال متعددة أن معظم هذه التوقعات تلتقي بالكاد وتشير إلى أن هناك حاجة إلى مزيد من العمل لتحليل السلوك الحالي لأنظمة المجالات المتعددة وجعلها تمسك بوعودها بالكامل.
When building machine translation systems, one often needs to make the best out of heterogeneous sets of parallel data in training, and to robustly handle inputs from unexpected domains in testing. This multi-domain scenario has attracted a lot of recent work that fall under the general umbrella of transfer learning. In this study, we revisit multi-domain machine translation, with the aim to formulate the motivations for developing such systems and the associated expectations with respect to performance. Our experiments with a large sample of multi-domain systems show that most of these expectations are hardly met and suggest that further work is needed to better analyze the current behaviour of multi-domain systems and to make them fully hold their promises.
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