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Register a new userDecomposing Complex Questions Makes Multi-Hop QA Easier and More Interpretable
تتحلل الأسئلة المعقدة تجعل Hop Hop QA أسهل وأكثر تفسيرا
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يتطلب Multi-Hop QA آلة للإجابة على أسئلة معقدة من خلال إيجاد أدلة وتعزيز متعددة، وتوفير الأدلة التوضيحية لإظهار عملية التفكير في الجهاز.نقترح قارئ مستخرج العلاقات والمقارنة (RERC)، وإطار من ثلاثة مراحل بناء على التحلل السؤال المعقد.يتحلل النازع العلاقة السؤال المعقد، ثم يجيب القارئ على الأسئلة الفرعية بدوره، وأخيرا ينفذ المقارنة مقارنة عدديا ويلخص كل شيء للحصول على الإجابة النهائية، حيث تشكل العملية بأكملها مسار أدلة منطق كامل.في DataSet 2wikimultihopqa، حقق نموذج RERC لدينا الأداء الحديثة، مع درجة فوز F1 المشتركة من 53.58 على المتصدرين.جميع مؤشرات RERC لدينا قريبة من الأداء البشري، مع 1.95 فقط خلف المستوى الإنساني في درجة F1 من حقيقة الدعم.في الوقت نفسه، فإن مسار الأدلة المقدم من إطار RERC لدينا له قابلية قراءة ممتازة وإخلاص.
Multi-hop QA requires the machine to answer complex questions through finding multiple clues and reasoning, and provide explanatory evidence to demonstrate the machine's reasoning process. We propose Relation Extractor-Reader and Comparator (RERC), a three-stage framework based on complex question decomposition. The Relation Extractor decomposes the complex question, and then the Reader answers the sub-questions in turn, and finally the Comparator performs numerical comparison and summarizes all to get the final answer, where the entire process itself constitutes a complete reasoning evidence path. In the 2WikiMultiHopQA dataset, our RERC model has achieved the state-of-the-art performance, with a winning joint F1 score of 53.58 on the leaderboard. All indicators of our RERC are close to human performance, with only 1.95 behind the human level in F1 score of support fact. At the same time, the evidence path provided by our RERC framework has excellent readability and faithfulness.
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