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Register a new userOpen-Domain Question Answering Goes Conversational via Question Rewriting
السؤال المفتوح المجال الرد يذهب يتحدث عن طريق إعادة كتابة الأسئلة
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نقدم مجموعة بيانات جديدة لإعادة كتابة الأسئلة في سياق المحادثة (QRECC)، والتي تحتوي على محادثات 14 ألف مع أزواج من الإجابات السؤال 80k.تتمثل المهمة في QRECC في العثور على إجابات على أسئلة المحادثة داخل مجموعة من صفحات الويب 10 أمتار (تقسيم إلى 54 مترا مربعا).قد يتم توزيع إجابات على الأسئلة الموجودة في نفس المحادثة عبر العديد من صفحات الويب.توفر QRECC التعليقات التوضيحية التي تسمح لنا بتدريب وتقييم المهارات الفرعية الفردية من إعادة كتابة السؤال، واسترجاع المرور وفهم القراءة المطلوبة لمهمة الإجابة على مسألة المحادثة نهاية إلى نهاية.نبلغ عن فعالية نهج خط الأساس القوي الذي يجمع بين النموذج الحديثة لإعادة كتابة الأسئلة والنماذج التنافسية لقضاء ضمان الجودة المفتوحة.حددت نتائجنا أول خط أساسي ل DataSet QRECC مع F1 من 19.10، مقارنة بمضابط العلوي البشري 75.45، مما يدل على صعوبة الإعداد وغرفة كبيرة للتحسين.
We introduce a new dataset for Question Rewriting in Conversational Context (QReCC), which contains 14K conversations with 80K question-answer pairs. The task in QReCC is to find answers to conversational questions within a collection of 10M web pages (split into 54M passages). Answers to questions in the same conversation may be distributed across several web pages. QReCC provides annotations that allow us to train and evaluate individual subtasks of question rewriting, passage retrieval and reading comprehension required for the end-to-end conversational question answering (QA) task. We report the effectiveness of a strong baseline approach that combines the state-of-the-art model for question rewriting, and competitive models for open-domain QA. Our results set the first baseline for the QReCC dataset with F1 of 19.10, compared to the human upper bound of 75.45, indicating the difficulty of the setup and a large room for improvement.
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