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Register a new userSPARTA: Efficient Open-Domain Question Answering via Sparse Transformer Matching Retrieval
Sparta: سؤالا فعالا مفتوحا في المجال الرد عن طريق استرجاع محول متفرق
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نقدم سبارتا، وهي طريقة استرجاع عصبي رواية تعرض وعد كبير في الأداء والتعميم والتفسيرية للحصول على سؤال السؤال المفتوح. على عكس العديد من أساليب التصنيف العصبي التي تستخدم ناقلات كثيفة أقرب بحث جار، يتعلم سبارتا تمثيل متقطع يمكن تنفيذها بكفاءة كمؤشر مقلوب. تمكن التمثيل الناتج استرجاع العصبي القابل للتطوير الذي لا يتطلب البحث التقريبي باهظ التقريب ويؤدي إلى أداء أفضل من نظيره الكثيف. لقد صادقنا مناهجنا على مهام الإجابة على سؤال (OpenQA) 4 و 11 مهام الإجابة على السؤال (REQA) 11. يحقق Sparta أحدث نتائج جديدة في مجموعة متنوعة من الأسئلة المفتوحة في المجال الرد على المهام في كل من مجموعات البيانات الإنجليزية والصينية، بما في ذلك الفريق المفتوح، CMRC، إلخ. تؤكد التحليل أيضا أن الطريقة المقترحة تخلق تمثيل قابل للتفسير الإنسان ويسمح السيطرة على المفاضلة بين الأداء والكفاءة.
We introduce SPARTA, a novel neural retrieval method that shows great promise in performance, generalization, and interpretability for open-domain question answering. Unlike many neural ranking methods that use dense vector nearest neighbor search, SPARTA learns a sparse representation that can be efficiently implemented as an Inverted Index. The resulting representation enables scalable neural retrieval that does not require expensive approximate vector search and leads to better performance than its dense counterpart. We validated our approaches on 4 open-domain question answering (OpenQA) tasks and 11 retrieval question answering (ReQA) tasks. SPARTA achieves new state-of-the-art results across a variety of open-domain question answering tasks in both English and Chinese datasets, including open SQuAD, CMRC and etc. Analysis also confirms that the proposed method creates human interpretable representation and allows flexible control over the trade-off between performance and efficiency.
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