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Register a new userContextual Rephrase Detection for Reducing Friction in Dialogue Systems
اكتشاف إعادة صياغة السياق للحد من الاحتكاك في أنظمة الحوار
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للمساعدين الصوتيين مثل Alexa ومساعد Google و SIRI ويزويف نوايا المستخدمين بشكل صحيح أهمية قصوى.ومع ذلك، يعاني المستخدمون في بعض الأحيان الاحتكاك مع هؤلاء المساعدين، بسبب الأخطاء من مكونات النظام المختلفة أو أخطاء المستخدمين مثل زلات اللسان.يميل المستخدمون إلى إعادة صياغة استفساراتهم حتى يحصلون على استجابة مرضية.يستخدم الكشف عن صياغة إعادة صياغة لتحديد إعادة التسلياع وتم التعامل معها منذ فترة طويلة كمهمة مع مدخلات الزوجية، والتي لا تستخدم بالكامل المعلومات السياقية (E.G. المستخدمين الضمنية).تحقيقا لهذه الغاية، نقترح نموذج اكتشاف صياغة صياغة حديثة لتحديد إعادة التسليح تلقائيا من الحوارات متعددة الدوران.نعرض كيفية الاستفادة من سياق الحوار وإشارات التفاعل وكيل المستخدم، بما في ذلك ملاحظات المستخدم الضمنية والفجوة الزمنية بين المنعطفات المختلفة، والتي يمكن أن تساعد بشكل كبير بشكل كبير في نماذج الكشف عن إعادة صياغة الزوجية.
For voice assistants like Alexa, Google Assistant, and Siri, correctly interpreting users' intentions is of utmost importance. However, users sometimes experience friction with these assistants, caused by errors from different system components or user errors such as slips of the tongue. Users tend to rephrase their queries until they get a satisfactory response. Rephrase detection is used to identify the rephrases and has long been treated as a task with pairwise input, which does not fully utilize the contextual information (e.g. users' implicit feedback). To this end, we propose a contextual rephrase detection model ContReph to automatically identify rephrases from multi-turn dialogues. We showcase how to leverage the dialogue context and user-agent interaction signals, including the user's implicit feedback and the time gap between different turns, which can help significantly outperform the pairwise rephrase detection models.
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