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Register a new userMonotonic Simultaneous Translation with Chunk-wise Reordering and Refinement
الترجمة المتزامنة الروماتونية مع إعادة ترتيب القمامة والحكم
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غالبا ما يتم تدريب العمل الحديث في الترجمة الآلية المتزامنة مع ترجمة جملة كاملة للجمدة التقليدية، مما يؤدي إلى الكمون المفرط أو الضرورة لتوقع الكلمات التي لا تعمل حتى الآن، عند التعامل مع زوج لغة تختلف أوامر الكلمات بشكل كبير.هذا على عكس الترجمة الفورية البشرية التي تنتج ترجمات راهبة إلى حد كبير على حساب الحكم النحوي من جملة تترجم.في هذه الورقة، بحيث نقترح خوارزمية لإعادة ترتيب وتحسين الجانب المستهدف من كوربوس ترجمة جملة كاملة، بحيث تتم محاذاة الكلمات / العبارات بين المصدر والجمل المستهدفة إلى حد كبير إلى حد كبير، باستخدام محاذاة Word والترجمة الآلية العصبية غير التلقائيةوبعدبعد ذلك، قم بتدريب نموذج الترجمة المتزامن المستخدمة على نطاق واسع في هذه الكائنات التي تم إعادة ترتيبها وإعادة ترتيبها.يؤدي النهج المقترح إلى تحسين درجات BLEU والترجمات الناتجة مع إظهار عائدي معززة بالجمل المصدر.
Recent work in simultaneous machine translation is often trained with conventional full sentence translation corpora, leading to either excessive latency or necessity to anticipate as-yet-unarrived words, when dealing with a language pair whose word orders significantly differ. This is unlike human simultaneous interpreters who produce largely monotonic translations at the expense of the grammaticality of a sentence being translated. In this paper, we thus propose an algorithm to reorder and refine the target side of a full sentence translation corpus, so that the words/phrases between the source and target sentences are aligned largely monotonically, using word alignment and non-autoregressive neural machine translation. We then train a widely used wait-k simultaneous translation model on this reordered-and-refined corpus. The proposed approach improves BLEU scores and resulting translations exhibit enhanced monotonicity with source sentences.
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