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Register a new userSource and Target Bidirectional Knowledge Distillation for End-to-end Speech Translation
المصدر والهدف تقطير المعرفة ثنائية الاتجاه لترجمة خطاب نهاية إلى نهاية
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تتمثل النهج التقليدي في تحسين أداء نماذج ترجمة الكلام في النهاية (E2E-St) في الاستفادة من النسخ المصدر عبر التدريب المسبق والتدريب المشترك مع التعرف على الكلام التلقائي (ASR) ومهام الترجمة الآلية العصبية (NMT). ومع ذلك، نظرا لأن طرائق الإدخال مختلفة، فمن الصعب الاستفادة من نص لغة المصدر بنجاح. في هذا العمل، نركز على تقطير المعرفة على مستوى التسلسل (SEQKD) من نماذج NMT الخارجية القائمة على النصوص. للاستفادة من الإمكانات الكاملة لمعلومات اللغة المصدر، نقترحنا على الوراء SEQKD، SEQKD من نموذج NMT للخلف إلى المصدر. تحقيقا لهذه الغاية، نقوم بتدريب نموذج ثنائي اللغة E2E-St للتنبؤ بالصايات بمثابة مهمة مساعدة مع وحدة فك ترميز واحدة. يتم إنشاء الصيغة من الترجمات في Bitex عبر الترجمة مرة أخرى. ونحن نقترح مزيدا من SEQKD ثنائي الاتجاه حيث يتم دمج SEQKD من نماذج NMT إلى الأمام والخلف. تظهر التقييمات التجريبية على كل من النماذج التلقائية وغير اللاحنة التلقائية أن Seqkd في كل اتجاه يحسن باستمرار أداء الترجمة، والفعالية مكملة بغض النظر عن القدرات النموذجية.
A conventional approach to improving the performance of end-to-end speech translation (E2E-ST) models is to leverage the source transcription via pre-training and joint training with automatic speech recognition (ASR) and neural machine translation (NMT) tasks. However, since the input modalities are different, it is difficult to leverage source language text successfully. In this work, we focus on sequence-level knowledge distillation (SeqKD) from external text-based NMT models. To leverage the full potential of the source language information, we propose backward SeqKD, SeqKD from a target-to-source backward NMT model. To this end, we train a bilingual E2E-ST model to predict paraphrased transcriptions as an auxiliary task with a single decoder. The paraphrases are generated from the translations in bitext via back-translation. We further propose bidirectional SeqKD in which SeqKD from both forward and backward NMT models is combined. Experimental evaluations on both autoregressive and non-autoregressive models show that SeqKD in each direction consistently improves the translation performance, and the effectiveness is complementary regardless of the model capacity.
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