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Register a new userDealing with training and test segmentation mismatch: FBK@IWSLT2021
التعامل مع عدم تطابق تجزئة التدريب والاختبار: FBK @ IWSLT2021
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توضح هذه الورقة تقديم نظام FBK إلى مهمة ترجمة الكلام IWSLT 2021. شاركنا نموذجا مباشرا، وهو عبارة عن بنية قائمة على المحولات المدربة لتترجم بيانات صوت الكلام باللغة الإنجليزية إلى نصوص ألمانية. يتميز خط أنابيب التدريب بتقطير المعرفة وإجراءات ضبط ناعم من خطوتين. يتم تنفيذ كل من تقطير المعرفة والخطوة الأولى الدقيقة على البيانات الحقيقية والاصطناعية مجزأة يدويا، والذي يتم إنشاؤه في هذا الأخير بنظام MT المدرب على الشركة المتاحة. بشكل مختلف، يتم تنفيذ خطوة التشغيل الدقيقة الثانية في تجزئة عشوائية ل DataSet MUST-C V2 EN-DE. هدفها الرئيسي هو تقليل قطرات الأداء التي تحدث عندما يتم تقييم نموذج الترجمة الكلام على البيانات المجزأة يدويا (I.E. لنفس الغرض، يتم تطبيق إجراء تجزئة هجينة مخصص تمثل كلا المحتوى الصوتي (توقف) وطول القطاعات المنتجة على بيانات الاختبار قبل تمريرها إلى النظام. في وقت الاستنتاج، قارننا هذا الإجراء مع طريقة تجزئة خط الأساس استنادا إلى اكتشاف نشاط الصوت (VAD). تشير نتائجنا إلى فعالية النهج الهجين المقترح، الذي أظهره تخفيض الفجوة مع تجزئة يدوية من نقاط بلو 8.3 إلى 1.4.
This paper describes FBK's system submission to the IWSLT 2021 Offline Speech Translation task. We participated with a direct model, which is a Transformer-based architecture trained to translate English speech audio data into German texts. The training pipeline is characterized by knowledge distillation and a two-step fine-tuning procedure. Both knowledge distillation and the first fine-tuning step are carried out on manually segmented real and synthetic data, the latter being generated with an MT system trained on the available corpora. Differently, the second fine-tuning step is carried out on a random segmentation of the MuST-C v2 En-De dataset. Its main goal is to reduce the performance drops occurring when a speech translation model trained on manually segmented data (i.e. an ideal, sentence-like segmentation) is evaluated on automatically segmented audio (i.e. actual, more realistic testing conditions). For the same purpose, a custom hybrid segmentation procedure that accounts for both audio content (pauses) and for the length of the produced segments is applied to the test data before passing them to the system. At inference time, we compared this procedure with a baseline segmentation method based on Voice Activity Detection (VAD). Our results indicate the effectiveness of the proposed hybrid approach, shown by a reduction of the gap with manual segmentation from 8.3 to 1.4 BLEU points.
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Recent researches show that pre-trained models (PTMs) are beneficial to Chinese Word Segmentation (CWS). However, PTMs used in previous works usually adopt language modeling as pre-training tasks, lacking task-specific prior segmentation knowledge an
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This paper contains the description for the submission of Karlsruhe Institute of Technology (KIT) for the multilingual TEDx translation task in the IWSLT 2021 evaluation campaign. Our main approach is to develop both cascade and end-to-end systems an
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