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Register a new userContext-aware Decoder for Neural Machine Translation using a Target-side Document-Level Language Model
CONTOTE-AWARE TECODER الترجمة الآلية العصبية باستخدام نموذج لغة الوثيقة ذات المستوى المستهدف
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على الرغم من أنه تم اقتراح العديد من نماذج الترجمة الآلية التي أدركها العديد من المناظر في إدراج سياقات مشتركة بين العلويين في الترجمة، يمكن تدريب هذه النماذج فقط في المجالات التي توجد فيها مستندات متوازية ذات محاذاة أساسيا.لذلك نقدم طريقة بسيطة لأداء فك تشفير السياق مع أي نموذج ترجمة مسبقا مسبقا مسبقا مسبقا باستخدام نموذج لغة مستوى المستند.تم بناء وحدة فك ترميز Context-Aware الخاص بنا على البيانات الموازية على مستوى الجملة والبيانات غير المباشرة على مستوى المستند على مستوى المستند.من وجهة نظر نظرية، فإن مساهمتنا الأساسية هي التمثيل الجديد لمعلومات السياقية باستخدام المعلومات المتبادلة النقطة بين السياق والحكم الحالي.نوضح فعالية طريقنا على الترجمة الإنجليزية إلى الترجمة الروسية، من خلال تقييمها مع اختبارات بلو وتناقض الترجمة من السياق.
Although many end-to-end context-aware neural machine translation models have been proposed to incorporate inter-sentential contexts in translation, these models can be trained only in domains where parallel documents with sentential alignments exist. We therefore present a simple method to perform context-aware decoding with any pre-trained sentence-level translation model by using a document-level language model. Our context-aware decoder is built upon sentence-level parallel data and target-side document-level monolingual data. From a theoretical viewpoint, our core contribution is the novel representation of contextual information using point-wise mutual information between context and the current sentence. We demonstrate the effectiveness of our method on English to Russian translation, by evaluating with BLEU and contrastive tests for context-aware translation.
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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.
Context-aware neural machine translation (NMT) incorporates contextual information of surrounding texts, that can improve the translation quality of document-level machine translation. Many existing works on context-aware NMT have focused on developi
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rophic forgetting, large domain divergence, and model explosion. To address these three problems, we propose a method of divide and conquer'' which is based on the importance of neurons or parameters for the translation model. In this method, we first prune the model and only keep the important neurons or parameters, making them responsible for both general-domain and in-domain translation. Then we further train the pruned model supervised by the original whole model with knowledge distillation. Last we expand the model to the original size and fine-tune the added parameters for the in-domain translation. We conducted experiments on different language pairs and domains and the results show that our method can achieve significant improvements compared with several strong baselines.
Recently a number of approaches have been proposed to improve translation performance for document-level neural machine translation (NMT). However, few are focusing on the subject of lexical translation consistency. In this paper we apply one transla
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