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Register a new userStudying The Impact Of Document-level Context On Simultaneous Neural Machine Translation
دراسة تأثير سياق مستوى المستند على الترجمة الآلية العصبية في وقت واحد
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في وضع الترجمة في الوقت الحقيقي للترجمة في الوقت الفعلي، تبدأ نماذج الترجمة الآلية العصبية (NMT) بتوليد الرموز الرموز اللغوية المستهدفة من جمل لغة مصدر غير كاملة وجعلها أكثر صعوبة في ترجمة وجودة الترجمة السيئة. أظهرت الأبحاث السابقة أن NMT على مستوى الوثيقة وتشمل الجملة والترميز السياق والكشف عن السياق من الجمل المجاورة ويساعد على تحسين جودة الترجمة. في إعدادات الترجمة المتزامنة، يجب أن يكون السياق من الجمل السابقة أكثر أهمية. تحقيقا لهذه الغاية وفي هذه الورقة، نقترح NMT على مستوى الوثيقة المتزامنة للانتظار حيث نحتفظ بمثابة تشفير السياق كما هو الحال واستبدال تشفير جملة المصدر ومكتشف اللغة المستهدف مع ما يعادله. نقوم بتجربة إعدادات الموارد المنخفضة والعالية باستخدام ALT و OPENSUBTITLES2018 Corpora وأين لاحظنا تحسينات طفيفة في جودة الترجمة. بعد ذلك إجراء تحليل للترجمات التي تم الحصول عليها باستخدام نماذجنا من خلال التركيز على الجمل التي يجب أن تستفيد من السياق حيث اكتشفنا أن النموذج يفعل وفي الواقع والاستفادة من السياق ولكنه غير قادر على الاستفادة من ذلك بشكل فعال وخاصة في انخفاض إعداد الموارد. هذا يدل على أن هناك حاجة لمزيد من الابتكار في طريقة تحديد السياق المفيد والاستفادة منها.
In a real-time simultaneous translation setting and neural machine translation (NMT) models start generating target language tokens from incomplete source language sentences and making them harder to translate and leading to poor translation quality. Previous research has shown that document-level NMT and comprising of sentence and context encoders and a decoder and leverages context from neighboring sentences and helps improve translation quality. In simultaneous translation settings and the context from previous sentences should be even more critical. To this end and in this paper and we propose wait-k simultaneous document-level NMT where we keep the context encoder as it is and replace the source sentence encoder and target language decoder with their wait-k equivalents. We experiment with low and high resource settings using the ALT and OpenSubtitles2018 corpora and where we observe minor improvements in translation quality. We then perform an analysis of the translations obtained using our models by focusing on sentences that should benefit from the context where we found out that the model does and in fact and benefit from context but is unable to effectively leverage it and especially in a low-resource setting. This shows that there is a need for further innovation in the way useful context is identified and leveraged.
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