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Register a new userEncoding Explanatory Knowledge for Zero-shot Science Question Answering
ترميز المعرفة التوضيحية لسؤال العلوم الصفرية
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تصف هذه الورقة N-XKT (الترميز العصبي بناء على نقل المعرفة التوضيحية)، وهي طريقة جديدة للتحويل التلقائي للمعرفة التوضيحية من خلال آليات الترميز العصبي.نوضح أن N-XKT قادر على تحسين الدقة والتعميم بشأن الإجابة على سؤال العلوم (QA).على وجه التحديد، من خلال الاستفادة من الحقائق من معرض المعرفة التوضيحية الخلفية، يظهر نموذج N-XKT تحسنا واضحا على QA صفر النار.علاوة على ذلك، نظهر أن N-XKT يمكن ضبطها بشكل جيد على مجموعة بيانات QA المستهدفة، مما يتيح التقارب الأسرع والنتائج الأكثر دقة.يتم إجراء تحليل منهجي لتحليل أداء نموذج N-XKT وتأثير فئات مختلفة من المعرفة حول مهمة تعميم الشوط الصفرية.
This paper describes N-XKT (Neural encoding based on eXplanatory Knowledge Transfer), a novel method for the automatic transfer of explanatory knowledge through neural encoding mechanisms. We demonstrate that N-XKT is able to improve accuracy and generalization on science Question Answering (QA). Specifically, by leveraging facts from background explanatory knowledge corpora, the N-XKT model shows a clear improvement on zero-shot QA. Furthermore, we show that N-XKT can be fine-tuned on a target QA dataset, enabling faster convergence and more accurate results. A systematic analysis is conducted to quantitatively analyze the performance of the N-XKT model and the impact of different categories of knowledge on the zero-shot generalization task.
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Multilingual question answering over knowledge graph (KGQA) aims to derive answers from a knowledge graph (KG) for questions in multiple languages. To be widely applicable, we focus on its zero-shot transfer setting. That is, we can only access train
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