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Register a new userHow does BERT process disfluency?
كيف تقوم برت عملية التنظير؟
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محادثات طبيعية مليئة التدقيق.تحقق هذه الدراسة إذا وتفهم برت وكيفية التنقيس بثلاث تجارب: (1) دراسة سلوكية باستخدام مهمة نهرية، (2) تحليل ل Aregbeddings و (3) تحليل لآلية الاهتمام على التنقيس.توضح الدراسة السلوكية أنه بدون ضبط جيد على البيانات النظافة، لا يعاني بيرت خسارة كبيرة من الأداء عند تقديمها مقارنة بالمدخلات بطلاقة (EXP1).يكشف التحليل على أزواج الجملة الجماعية والجوزاء بطلاقة أن الطبقة الأعمق، كلما زاد مماثلة تمثيلها (EXP2).يشير هذا إلى أن الطبقات العميقة من بيرت تصبح ثابتا نسبيا للتنقيس.نحن نحدد الاهتمام كآلية محتملة يمكن أن تفسر هذه الظاهرة (EXP3).بشكل عام، تشير الدراسة إلى أن بيرت لديه معرفة بنية التنظير.نؤكد على إمكانية استخدام بيرت لفهم الكلام الطبيعي دون إزالة التنظير.
Natural conversations are filled with disfluencies. This study investigates if and how BERT understands disfluency with three experiments: (1) a behavioural study using a downstream task, (2) an analysis of sentence embeddings and (3) an analysis of the attention mechanism on disfluency. The behavioural study shows that without fine-tuning on disfluent data, BERT does not suffer significant performance loss when presented disfluent compared to fluent inputs (exp1). Analysis on sentence embeddings of disfluent and fluent sentence pairs reveals that the deeper the layer, the more similar their representation (exp2). This indicates that deep layers of BERT become relatively invariant to disfluency. We pinpoint attention as a potential mechanism that could explain this phenomenon (exp3). Overall, the study suggests that BERT has knowledge of disfluency structure. We emphasise the potential of using BERT to understand natural utterances without disfluency removal.
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