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Register a new userLooking Beyond Sentence-Level Natural Language Inference for Question Answering and Text Summarization
تبحث وراء الاستدلال باللغة الطبيعية على مستوى الجملة للحصول على سؤال الرد وتلخيص النص
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حقق استنتاج اللغة الطبيعي (NLI) اهتماما كبيرا في السنوات الأخيرة؛ومع ذلك، ظل وعد تطبيق اختراقات NLI لمهام NLP الأخرى المنفذة غير الموحدة.في هذا العمل، نستخدم الفهم القروض متعدد الخيارات (MCRC) وفحص صحة واقعية لمهام التلخيص النصي (CFCS) للتحقيق في الأسباب المحتملة لهذا.تظهر النتائج الخاصة بنا أن: (1) الطول الأقصر نسبيا في مجموعات بيانات NLI التقليدية هو التحدي الرئيسي الذي يحظر الاستخدام في تطبيقات المصب (التي تفعل أفضل مع سياقات أطول)؛(2) يمكن معالجة هذا التحدي عن طريق تحويل مجموعات بيانات فهم القراءة الغنية بالموارد إلى مجموعات بيانات NLI أطول؛و (3) تتفوق النماذج المدربة على مجموعات بيانات الفرضية المحولة والأطول الفرضية تلك المدربة باستخدام مجموعات بيانات NLI التقليدية القصيرة في مهام المصب في المقام الأول بسبب الفرق في أطوال الفرضية.
Natural Language Inference (NLI) has garnered significant attention in recent years; however, the promise of applying NLI breakthroughs to other downstream NLP tasks has remained unfulfilled. In this work, we use the multiple-choice reading comprehension (MCRC) and checking factual correctness of textual summarization (CFCS) tasks to investigate potential reasons for this. Our findings show that: (1) the relatively shorter length of premises in traditional NLI datasets is the primary challenge prohibiting usage in downstream applications (which do better with longer contexts); (2) this challenge can be addressed by automatically converting resource-rich reading comprehension datasets into longer-premise NLI datasets; and (3) models trained on the converted, longer-premise datasets outperform those trained using short-premise traditional NLI datasets on downstream tasks primarily due to the difference in premise lengths.
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