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Register a new userEnhancing Dual-Encoders with Question and Answer Cross-Embeddings for Answer Retrieval
تعزيز التشفير المزدوج مع السؤال والإجابة على التضامن المتبادلة لاسترجاع الإجابة
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Dual-Encoders هي آلية واعدة لاسترجاع الإجابة في أنظمة الإجابة على الأسئلة (QA). حاليا معظم التشفير المزدوج التقليدية تعلم التمثيل الدلالي للأسئلة والأجوبة فقط من خلال نقاط مطابقة. اقترح الباحثون تقديم ميزات تفاعلات ضمان الجودة في وظيفة التهديف ولكن بتكلفة منخفضة الكفاءة في مرحلة الاستدلال. للحفاظ على الترميز المستقل للأسئلة والأجوبة أثناء مرحلة الاستدلال، يتم تقديم التشفير التلقائي التلقائي بشكل أكبر لإعادة بناء الإجابات (الأسئلة) من Asceddings من السؤال (الإجابة) بمثابة مهمة مساعدة لتعزيز تفاعل QA في مرحلة التدريب في مرحلة التدريب في مرحلة التدريب في مرحلة التدريب في مرحلة التدريب. ومع ذلك، فإن احتياجات جيل النص واسترجاع الإجابة مختلفة، مما يؤدي إلى صلابة في التدريب. في هذا العمل، نقترح إطارا لتعزيز نموذج المشفرين المزدوجين مع الإجابة على السؤال وآلية محاذاة هندسية جديدة (GAM) لمواءمة الهندسة من المدينات من الترميز المزدوج مع ذلك من التشفير عبر التشفير. تظهر النتائج التجريبية الواسعة أن إطارنا يحسن بشكل كبير من طراز الترميز المزدوج وتفوق على الطريقة التي تظهر على مجموعة بيانات استرجاع الإجابة المتعددة.
Dual-Encoders is a promising mechanism for answer retrieval in question answering (QA) systems. Currently most conventional Dual-Encoders learn the semantic representations of questions and answers merely through matching score. Researchers proposed to introduce the QA interaction features in scoring function but at the cost of low efficiency in inference stage. To keep independent encoding of questions and answers during inference stage, variational auto-encoder is further introduced to reconstruct answers (questions) from question (answer) embeddings as an auxiliary task to enhance QA interaction in representation learning in training stage. However, the needs of text generation and answer retrieval are different, which leads to hardness in training. In this work, we propose a framework to enhance the Dual-Encoders model with question answer cross-embeddings and a novel Geometry Alignment Mechanism (GAM) to align the geometry of embeddings from Dual-Encoders with that from Cross-Encoders. Extensive experimental results show that our framework significantly improves Dual-Encoders model and outperforms the state-of-the-art method on multiple answer retrieval datasets.
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