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شبكة التفاعل السياق
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تم تحقيق معالم رائعة في نص مطابقة من خلال اعتماد آلية انتباه متقاطعة لالتقاط الروابط الدلالية ذات الصلة بين تمثيلين عقديين.ومع ذلك، يركز الاهتمام العادي عبر مستوى الروابط على مستوى الكلمات بين تسلسل المدخلات، وإهمال أهمية المعلومات السياقية.نقترح شبكة التفاعل المعرفة في السياق (عملة معدنية) لمحاذاة متسلسلتين بشكل صحيح وتستنتج علاقتها الدلالية.على وجه التحديد، يتضمن كل كتلة تفاعل (1) آلية اعتبارية إعلامية في السياق لإدماج المعلومات السياقية بفعالية عند محاذاة متتسلالات، و (2) طبقة انصهار بوابة لتمثيلات محاذاة محاذاة مرنة.نحن نطبق كتل تفاعلية مكدسة متعددة لإنتاج محاذاة على مستويات مختلفة وتحسين نتائج الانتباه تدريجيا.تجارب على اثنين من مجموعات بيانات مطابقة الأسئلة والتحليلات التفصيلية توضح فعالية نموذجنا.
Impressive milestones have been achieved in text matching by adopting a cross-attention mechanism to capture pertinent semantic connections between two sentence representations. However, regular cross-attention focuses on word-level links between the two input sequences, neglecting the importance of contextual information. We propose a context-aware interaction network (COIN) to properly align two sequences and infer their semantic relationship. Specifically, each interaction block includes (1) a context-aware cross-attention mechanism to effectively integrate contextual information when aligning two sequences, and (2) a gate fusion layer to flexibly interpolate aligned representations. We apply multiple stacked interaction blocks to produce alignments at different levels and gradually refine the attention results. Experiments on two question matching datasets and detailed analyses demonstrate the effectiveness of our model.
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