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Register a new userJoint Passage Ranking for Diverse Multi-Answer Retrieval
ترتيب مرور مشترك لاسترجاع الأجابة المتنوعة
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نقوم بدراسة استرجاع الأجابة المتعددة، وهي مشكلة غير استكشافية تتطلب استرجاع المقاطع لتغطية إجابات مميزة متعددة لسؤال معين. تتطلب هذه المهمة نمذجة مشتركة للممرات المستردة، حيث يجب ألا تسترجع النماذج مرارا وتكرارا الممرات التي تحتوي على نفس الإجابة بتكلفة مفقودة إجابة صالحة مختلفة. يعد العمل المسبق التركيز على استرجاع الإجابة الفردية محدودا لأنه لا يستطيع السبب في مجموعة المقاطعات المشتركة. في هذه الورقة، نقدم JPR، نموذج استرجاع مقطع مشترك يركز على إعادة إطلاق Reranking. لنموذج الاحتمال المشترك للممرات المستردة، يستخدم JPR لاستخدام Reranker التلقائي الذي يختار تسلسل من المقاطع، ومجهز بخوارزميات تدريب جديدة وفك تشفيرها. بالمقارنة مع النهج المسبقة، يحقق JPR تغطية إجابة أفضل بكثير على ثلاثة مجموعات بيانات متعددة الإجابات. عند الجمع بين مسألة السؤال المصب، يتيح الاسترجاع المحسن نماذج توليد الإجابات الأكبر لأنها تحتاج إلى النظر في عدد أقل من المقاطع، وإنشاء حالة جديدة من بين الفن.
We study multi-answer retrieval, an under-explored problem that requires retrieving passages to cover multiple distinct answers for a given question. This task requires joint modeling of retrieved passages, as models should not repeatedly retrieve passages containing the same answer at the cost of missing a different valid answer. Prior work focusing on single-answer retrieval is limited as it cannot reason about the set of passages jointly. In this paper, we introduce JPR, a joint passage retrieval model focusing on reranking. To model the joint probability of the retrieved passages, JPR makes use of an autoregressive reranker that selects a sequence of passages, equipped with novel training and decoding algorithms. Compared to prior approaches, JPR achieves significantly better answer coverage on three multi-answer datasets. When combined with downstream question answering, the improved retrieval enables larger answer generation models since they need to consider fewer passages, establishing a new state-of-the-art.
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In various natural language processing tasks, passage retrieval and passage re-ranking are two key procedures in finding and ranking relevant information. Since both the two procedures contribute to the final performance, it is important to jointly o
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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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