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Register a new userTowards Incremental Transformers: An Empirical Analysis of Transformer Models for Incremental NLU
نحو المحولات الإضافية: تحليل تجريبي لنماذج المحولات ل NLU تدريجي
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تتيح المعالجة الإضافية أنظمة تفاعلية تستجيب بناء على المدخلات الجزئية، وهي خاصية مرغوبة على سبيل المثال في عوامل الحوار. تقوم بنية المحولات الشعبية حاليا بطبيعتها بمعالجة التسلسلات ككل، تجرد فكرة الوقت. محاولات العمل الحديثة لتطبيق المحولات بشكل تدريجي عن طريق إعادة التشغيل - تدريجيا من خلال التغذية بشكل متكرر، إلى نموذج غير متقلب، بادئات إدخال أطول بشكل متزايد لإنتاج مخرجات جزئية. ومع ذلك، فإن هذا النهج مكلف بشكل حسابي ولا يتجادل بكفاءة للتسلسل الطويل. بالتوازي، نشهد جهود لجعل المحولات أكثر كفاءة، على سبيل المثال المحول الخطي (LT) مع آلية تكرار. في هذا العمل، ندرس جدوى LT ل NLU تدريجي باللغة الإنجليزية. تبين نتائجنا أن نموذج LT المتكرر لديه أفضل أداء تدريجي وسرعة الاستدلال أسرع مقارنة بالمحول القياسي واللفنت مع إعادة التشغيل التدريجي، بتكلفة جزء من جودة غير متزايدة (التسلسل الكامل). نظرا لأن إسقاط الأداء يمكن تخفيفه عن طريق تدريب النموذج لانتظار السياق الصحيح قبل الالتزام بإخراج وأن التدريب بادئة الإدخال مفيد لتقديم المخرجات الجزئية الصحيحة.
Incremental processing allows interactive systems to respond based on partial inputs, which is a desirable property e.g. in dialogue agents. The currently popular Transformer architecture inherently processes sequences as a whole, abstracting away the notion of time. Recent work attempts to apply Transformers incrementally via restart-incrementality by repeatedly feeding, to an unchanged model, increasingly longer input prefixes to produce partial outputs. However, this approach is computationally costly and does not scale efficiently for long sequences. In parallel, we witness efforts to make Transformers more efficient, e.g. the Linear Transformer (LT) with a recurrence mechanism. In this work, we examine the feasibility of LT for incremental NLU in English. Our results show that the recurrent LT model has better incremental performance and faster inference speed compared to the standard Transformer and LT with restart-incrementality, at the cost of part of the non-incremental (full sequence) quality. We show that the performance drop can be mitigated by training the model to wait for right context before committing to an output and that training with input prefixes is beneficial for delivering correct partial outputs.
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