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Register a new userWhen Retriever-Reader Meets Scenario-Based Multiple-Choice Questions
عند المسترد القارئ يلتقي أسئلة متعددة الخيارات القائمة على السيناريو
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يتطلب إجابة السؤال المستندة إلى السيناريو (SQA) على استرداد وقراءة الفقرات من كوربوس كبيرة للإجابة على سؤال محكوم بموجب وصف سيناريو طويل.نظرا لأن السيناريو يحتوي على كلا القصاصات الاسترجاع والكثير من الضوضاء، فإن استرجاع SQA صعب للغاية.علاوة على ذلك، بالكاد يمكن أن يشرف عليه بسبب عدم وجود ملصقات ذات صلة من الفقرات ل SQA.لتلبية التحدي، في هذه الورقة نقترح نموذج قارئ مسترجع مشترك يسمى Jeeves حيث يتم الإشراف على المسترد الضمني فقط باستخدام ملصقات ضمان الجودة عبر آلية ترجيح كلمة جديدة.يتفوق Jeeves بشكل كبير على مجموعة متنوعة من خطوط الأساس القوية على أسئلة متعددة الخيارات في ثلاث مجموعات بيانات SQA.
Scenario-based question answering (SQA) requires retrieving and reading paragraphs from a large corpus to answer a question which is contextualized by a long scenario description. Since a scenario contains both keyphrases for retrieval and much noise, retrieval for SQA is extremely difficult. Moreover, it can hardly be supervised due to the lack of relevance labels of paragraphs for SQA. To meet the challenge, in this paper we propose a joint retriever-reader model called JEEVES where the retriever is implicitly supervised only using QA labels via a novel word weighting mechanism. JEEVES significantly outperforms a variety of strong baselines on multiple-choice questions in three SQA datasets.
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