إن القدرة على توليد أسئلة التوضيح I.E.، أسئلة تحدد المعلومات المفقودة المفيدة في سياق معين، مهمة في الحد من الغموض.يستخدم البشر تجربة سابقة مع سياقات مماثلة لتشكيل وجهة نظر عالمية ومقارنين السياق المعدد للتأكد من مفقود وما هو مفيد في السياق.مستوحاة من ذلك، نقترح نموذجا لتدوين سؤال التوضيح حيث نحدد أولا ما هو مفقود عن طريق اختلاف الفرق بين المنظر العالمي والمحلي ثم تدريب نموذج لتحديد ما هو مفيد وتوليد سؤال حوله.تتفوق نموذجنا على العديد من خطوط الأساس كما يحكم عليها كل من المقاييس التلقائية والبشر.
The ability to generate clarification questions i.e., questions that identify useful missing information in a given context, is important in reducing ambiguity. Humans use previous experience with similar contexts to form a global view and compare it to the given context to ascertain what is missing and what is useful in the context. Inspired by this, we propose a model for clarification question generation where we first identify what is missing by taking a difference between the global and the local view and then train a model to identify what is useful and generate a question about it. Our model outperforms several baselines as judged by both automatic metrics and humans.
References used
https://aclanthology.org/
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