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Register a new userInverted Projection for Robust Speech Translation
ترجمة مقلوبة ترجمة خطاب قوية
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تقوم أنظمة الترجمة التقليدية المدربين على وثائق مكتوبة بشكل جيد للترجمة المستندة إلى النصوص ولكن ليس كذلك للتطبيقات المستندة إلى الكلام.نحن نهدف إلى تكييف نماذج الترجمة إلى الكلام عن طريق إدخال أخطاء معجمية حقيقية من أخطاء ASR والتجزئة من علامات الترقيم التلقائية في بيانات تدريب الترجمة الخاصة بنا.نقدم نهج إسقاط مقلوب تم اكتشافه تلقائيا شرائح النظام تلقائيا على النصوص البشرية ومن ثم إعادة الشرائح ترجمة الذهب إلى محاذاة النصوص الإنسانية المتوقعة.نوضح أن هذا يتغلب على عدم تطابق اختبار القطار الموجود في نهج التدريب الأخرى.يحقق نهج الإسقاط الجديد مكاسب أكثر من 1 نقطة بلو على خط أساس يتعرض للنصوص البشرية والجزء، وهذه المكاسب تعقد لكلا بيانات IWSLT وبيانات YouTube.
Traditional translation systems trained on written documents perform well for text-based translation but not as well for speech-based applications. We aim to adapt translation models to speech by introducing actual lexical errors from ASR and segmentation errors from automatic punctuation into our translation training data. We introduce an inverted projection approach that projects automatically detected system segments onto human transcripts and then re-segments the gold translations to align with the projected human transcripts. We demonstrate that this overcomes the train-test mismatch present in other training approaches. The new projection approach achieves gains of over 1 BLEU point over a baseline that is exposed to the human transcripts and segmentations, and these gains hold for both IWSLT data and YouTube data.
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