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Register a new userSearchable Hidden Intermediates for End-to-End Models of Decomposable Sequence Tasks
وسيطة مخفية للبحث عن النماذج نهاية إلى نهاية مهام التسلسل المتحلل
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الأساليب نهاية إلى نهاية لمهام التسلسل أصبحت شعبية بشكل متزايد. ومع ذلك بالنسبة لمهام التسلسل المعقدة، مثل ترجمة الكلام، فإن الأنظمة التي تتالي أن العديد من النماذج المدربة على المهام الفرعية قد أظهرت متفوقة، مما يشير إلى أن تكوين النظم المتتالية يبسط التعلم وتمكين قدرات البحث المتطورة. في هذا العمل، نقدم إطارا نهاية إلى نهائي يستغل التركيز لتعلم التمثيلات المخفية القابلة للبحث في المراحل المتوسطة لنموذج التسلسل باستخدام المهام الفرعية المتحللة. يمكن تحسين هذه الوسيط المخفي باستخدام بحث الشعاع لتعزيز الأداء العام ويمكنه أيضا دمج النماذج الخارجية في المراحل المتوسطة للشبكة لإعادة النتيجة أو التكيف باتجاه بيانات خارج المجال. مثيل واحد من الإطار المقترح هو نموذج متعدد اللمعان لترجمة الكلام التي تستخرج الوسطيات المخفية القابلة للبحث عن مهمة فرعية للتعرف على الكلام. يوضح النموذج الفوائد المذكورة أعلاه وتفوق على الحالة السابقة من بين الفن من خلال +6 و +3 بلو على مجموعتي الاختبار من Fisher-Callhome وحوالي +3 و +4 بلو على اللغة الإنجليزية والألمانية والإنجليزية - مجموعات اختبار فرنسية من must-c.
End-to-end approaches for sequence tasks are becoming increasingly popular. Yet for complex sequence tasks, like speech translation, systems that cascade several models trained on sub-tasks have shown to be superior, suggesting that the compositionality of cascaded systems simplifies learning and enables sophisticated search capabilities. In this work, we present an end-to-end framework that exploits compositionality to learn searchable hidden representations at intermediate stages of a sequence model using decomposed sub-tasks. These hidden intermediates can be improved using beam search to enhance the overall performance and can also incorporate external models at intermediate stages of the network to re-score or adapt towards out-of-domain data. One instance of the proposed framework is a Multi-Decoder model for speech translation that extracts the searchable hidden intermediates from a speech recognition sub-task. The model demonstrates the aforementioned benefits and outperforms the previous state-of-the-art by around +6 and +3 BLEU on the two test sets of Fisher-CallHome and by around +3 and +4 BLEU on the English-German and English-French test sets of MuST-C.
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