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Register a new userSummarize-then-Answer: Generating Concise Explanations for Multi-hop Reading Comprehension
تلخيص - ثم الإجابة: توليد تفسيرات موجزة لفهم القراءة متعددة القفز
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كيف يمكننا أن نولد تفسيرات موجزة لفهم القراءة متعددة القفز (RC)؟ يمكن اعتبار الاستراتيجيات الحالية لتحديد الجمل الداعمة كأخصات تركز على الأسئلة الاستخراجية لنص المدخلات. ومع ذلك، فإن هذه التفسيرات الاستخراجية ليست بالضرورة موجزة، وليس كافية على الأقل للإجابة على سؤال. بدلا من ذلك، ندعو إلى نهج إغراق، حيث نقترح إنشاء ملخص ركز على أسئلة غير مركزة لفقرات الإدخال ثم إطعامها لنظام RC. بالنظر إلى كمية محدودة من التفسيرات الموضحة البشرية المشروح البشرية، فإننا نربع الشرح الإفجي بطريقة شبه إشراف، حيث نبدأ من النموذج الخاضع للإشراف، ثم تدريب عليه من خلال التجربة والخطأ تعظيم وظيفة المكافآت التي تمت ترقيتها على الحجز. توضح تجاربنا أن المفسر المفسد المقترح يمكن أن يولد توضيحات أكثر إحكاما من الشرح الاستخراجي مع إشراف محدود (مثيلات 2K فقط) مع الحفاظ على الاكتفاء.
How can we generate concise explanations for multi-hop Reading Comprehension (RC)? The current strategies of identifying supporting sentences can be seen as an extractive question-focused summarization of the input text. However, these extractive explanations are not necessarily concise i.e. not minimally sufficient for answering a question. Instead, we advocate for an abstractive approach, where we propose to generate a question-focused, abstractive summary of input paragraphs and then feed it to an RC system. Given a limited amount of human-annotated abstractive explanations, we train the abstractive explainer in a semi-supervised manner, where we start from the supervised model and then train it further through trial and error maximizing a conciseness-promoted reward function. Our experiments demonstrate that the proposed abstractive explainer can generate more compact explanations than an extractive explainer with limited supervision (only 2k instances) while maintaining sufficiency.
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