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Register a new userOpen-Domain Question Answering with Pre-Constructed Question Spaces
السؤال المفتوح المجال الرد على مساحات السؤال المبنية مسبقا
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يسأل الأسئلة المفتوحة الإجابة على تحديد إجابات الأسئلة التي أنشأتها المستخدم في مجموعات ضخمة من المستندات. أساليب Readriever-Reverse Graph النهج هي أسران كبيرتان من الحلول لهذه المهمة. يطبق قارئ المسترد أولا تقنيات استرجاع المعلومات للحصول على تحديد عدد قليل من الممرات التي من المحتمل أن تكون ذات صلة، ثم تغذي النص المسترد إلى قارئ شبكة عصبي لاستخراج الإجابة. بدلا من ذلك، يمكن بناء الرسوم البيانية المعرفة واستفسارها للإجابة على أسئلة المستخدمين. نقترح خوارزمية مع تصميم رواية Reader-Reader - يختلف عن كل من العائلات. يستخدم Reader-Retriever أولا قارئ حاليا لقراءة الكائن وإنشاء مجموعات من جميع الأسئلة المجدية المرتبطة بإجاباتهم، ثم يستخدم المسترد عبر الإنترنت للاستجابة لاستعلامات المستخدم من خلال البحث في مساحات الأسئلة التي تم إنشاؤها مسبقا للحصول على إجابات أكثر احتمالا أن يطلب في الطريقة المحددة. ندمج مزيد من الجمع بين قارئ المسترجع واحد واسترجاع القارئين في نموذج هجين يسمى R6 لأفضل أداء. تبين تجارب مع مجموعة بيانات عامة واسعة النطاق أن R6 يحقق دقة حديثة.
Open-domain question answering aims at locating the answers to user-generated questions in massive collections of documents. Retriever-readers and knowledge graph approaches are two big families of solutions to this task. A retriever-reader first applies information retrieval techniques to locate a few passages that are likely to be relevant, and then feeds the retrieved text to a neural network reader to extract the answer. Alternatively, knowledge graphs can be constructed and queried to answer users' questions. We propose an algorithm with a novel reader-retriever design that differs from both families. Our reader-retriever first uses an offline reader to read the corpus and generate collections of all answerable questions associated with their answers, and then uses an online retriever to respond to user queries by searching the pre-constructed question spaces for answers that are most likely to be asked in the given way. We further combine one retriever-reader and two reader-retrievers into a hybrid model called R6 for the best performance. Experiments with two large-scale public datasets show that R6 achieves state-of-the-art accuracy.
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