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Register a new userNeural Natural Logic Inference for Interpretable Question Answering
الاستدلال المنطحي الطبيعي العصبي للنظر القابل للتفسير الإجابة
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يمكن إلقاء العديد من الأسئلة المفتوحة على المشكلات بمثابة مهمة استقامة نصية، حيث يتم تسليم الإجابات السؤال والمرشح لتشكيل الفرضيات. ثم يحدد نظام ضمان الجودة إذا كان قواعد المعرفة الداعمة، التي تعتبر مباني محتملة، تنطوي على الفرضيات. في هذه الورقة، نحقق في نهج ضمان الجودة العصبي الرمزي الذي يدمج المنطق الطبيعي في مجال البندسة التعليمية العميقة، نحو تطوير نماذج إجابة فعالة وغير قابلة للتفسير. النموذج المقترح يسجل تدريجيا فرضية ومباني مرشحة بعد خطوات الاستدلال المنطقي الطبيعي لبناء مسارات إثبات. يتم قياس درجات الاستلام بين الفرضيات المتوسطة المكتسبة ومباني المرشح لتحديد ما إذا كانت الفرضية تستلزم الفرضية. نظرا لأن عملية التفكير الطبيعي للمنطق تشكل هيكل يشبه الأشجار وتسلسلا هرميا، فإننا قمنا بتضمين الفرضيات والمباني في مساحة مفرطة بدلا من مساحة Euclidean للحصول على تمثيلات أكثر دقة. تجريبيا، وطريقة لدينا تفوقت على العمل المسبق على الإجابة على أسئلة علوم متعددة الخيارات، وتحقيق أفضل النتائج في مجموعة بيانات متوفرة للجمهور. توفر عملية الاستدلال المنطقي الطبيعي بطبيعتها الأدلة للمساعدة في تفسير عملية التنبؤ.
Many open-domain question answering problems can be cast as a textual entailment task, where a question and candidate answers are concatenated to form hypotheses. A QA system then determines if the supporting knowledge bases, regarded as potential premises, entail the hypotheses. In this paper, we investigate a neural-symbolic QA approach that integrates natural logic reasoning within deep learning architectures, towards developing effective and yet explainable question answering models. The proposed model gradually bridges a hypothesis and candidate premises following natural logic inference steps to build proof paths. Entailment scores between the acquired intermediate hypotheses and candidate premises are measured to determine if a premise entails the hypothesis. As the natural logic reasoning process forms a tree-like, hierarchical structure, we embed hypotheses and premises in a Hyperbolic space rather than Euclidean space to acquire more precise representations. Empirically, our method outperforms prior work on answering multiple-choice science questions, achieving the best results on two publicly available datasets. The natural logic inference process inherently provides evidence to help explain the prediction process.
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