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Register a new userMulti-stage Training with Improved Negative Contrast for Neural Passage Retrieval
التدريب متعدد المراحل مع تحسن سالبة تباين لاسترجاع الممر العصبي
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في سياق استرجاع المرفق العصبي، ندرس ثلاث تقنيات واعدة: توليد البيانات الاصطناعية، أخذ العينات السلبية، والانصهار. نحن نحقق بشكل منهجي كيف تسهم هذه التقنيات في أداء نظام الاسترجاع وكيف تكمل بعضها البعض. نقترح إطارا متعدد المراحل يتكون من التدريب المسبق مع البيانات الاصطناعية، والضبط بشكل جيد مع البيانات المسمى، والأماينة السلبية في كلتا المرحلتين. نقوم بدراسة ست استراتيجيات أخذ العينات السلبية وتطبيقها على مرحلة ضبط الدقيقة، وكخادمة جديرة بالملاحظة، إلى البيانات الاصطناعية التي نستخدمها للتدريب المسبق. أيضا، نستكشف أساليب الانصهار التي تجمع بين السلبيات من استراتيجيات مختلفة. نقيم نظامنا باستخدام مهام استرجاع مرور اثنين للنطاق المفتوح واستخدام MS MARCO. تظهر تجاربنا أن زيادة التباين السلبي في كلتا المراحل فعالة لتحسين دقة استرجاع المرور، والأهم من ذلك، كما أنها تظهر أن توليد البيانات الاصطناعية والأماينة السلبية لها فوائد مضافة. علاوة على ذلك، فإن استخدام الانصهار من الأنواع المختلفة يسمح لنا بالوصول إلى الأداء الذي يحدد مستوى جديد من بين الفنادق في قسمين من المهام التي تقييمناها.
In the context of neural passage retrieval, we study three promising techniques: synthetic data generation, negative sampling, and fusion. We systematically investigate how these techniques contribute to the performance of the retrieval system and how they complement each other. We propose a multi-stage framework comprising of pre-training with synthetic data, fine-tuning with labeled data, and negative sampling at both stages. We study six negative sampling strategies and apply them to the fine-tuning stage and, as a noteworthy novelty, to the synthetic data that we use for pre-training. Also, we explore fusion methods that combine negatives from different strategies. We evaluate our system using two passage retrieval tasks for open-domain QA and using MS MARCO. Our experiments show that augmenting the negative contrast in both stages is effective to improve passage retrieval accuracy and, importantly, they also show that synthetic data generation and negative sampling have additive benefits. Moreover, using the fusion of different kinds allows us to reach performance that establishes a new state-of-the-art level in two of the tasks we evaluated.
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In open-domain question answering, dense passage retrieval has become a new paradigm to retrieve relevant passages for finding answers. Typically, the dual-encoder architecture is adopted to learn dense representations of questions and passages for s
emantic matching. However, it is difficult to effectively train a dual-encoder due to the challenges including the discrepancy between training and inference, the existence of unlabeled positives and limited training data. To address these challenges, we propose an optimized training approach, called RocketQA, to improving dense passage retrieval. We make three major technical contributions in RocketQA, namely cross-batch negatives, denoised hard negatives and data augmentation. The experiment results show that RocketQA significantly outperforms previous state-of-the-art models on both MSMARCO and Natural Questions. We also conduct extensive experiments to examine the effectiveness of the three strategies in RocketQA. Besides, we demonstrate that the performance of end-to-end QA can be improved based on our RocketQA retriever.
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