RocketQAv2: A Joint Training Method for Dense Passage Retrieval and Passage Re-ranking (2110.07367v2)

Abstract: 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 optimize them in order to achieve mutual improvement. In this paper, we propose a novel joint training approach for dense passage retrieval and passage re-ranking. A major contribution is that we introduce the dynamic listwise distillation, where we design a unified listwise training approach for both the retriever and the re-ranker. During the dynamic distillation, the retriever and the re-ranker can be adaptively improved according to each other's relevance information. We also propose a hybrid data augmentation strategy to construct diverse training instances for listwise training approach. Extensive experiments show the effectiveness of our approach on both MSMARCO and Natural Questions datasets. Our code is available at https://github.com/PaddlePaddle/RocketQA.

RocketQAv2: A Joint Training Method for Dense Passage Retrieval and Passage Re-ranking

The paper "RocketQAv2: A Joint Training Method for Dense Passage Retrieval and Passage Re-ranking" presents an advanced methodology for enhancing the performance of dense passage retrieval and passage re-ranking in NLP tasks. The approach is particularly relevant for areas such as question answering, dialogue systems, and entity linking, where efficiently identifying and ranking relevant information is crucial.

Core Contributions

The authors introduce a sophisticated joint training approach that simultaneously optimizes both dense passage retrieval and passage re-ranking. This work proposes two significant innovations:

1. Dynamic Listwise Distillation: The researchers have developed a unified listwise training mechanism that enables dynamic distillation. Here, both the retriever and re-ranker adaptively learn from each other's relevance distributions. By leveraging KL-divergence minimization between the retriever's and re-ranker's relevance scores, the approach facilitates mutual enhancement. Unlike previous methods that often froze one module, this dynamic scenario allows continuous optimization of both components.
2. Hybrid Data Augmentation: This strategy aims to provide diverse and high-quality training instances by combining random sampling with denoised sampling methodologies. The inclusion of hard negatives, derived from both random and RocketQA re-ranker filtered passages, ensures a comprehensive representation of passage distributions.

Experimental Evaluation

The authors provide extensive empirical evidence of the efficacy of RocketQAv2 using two large-scale datasets: MSMARCO and Natural Questions. The numerical results illustrate a substantial improvement in passage retrieval metrics, with RocketQAv2 achieving impressive MRR and recall scores across both datasets.

• On MSMARCO, the proposed retriever yielded an MRR@10 of 38.8 and a Recall@1000 of 98.1, outperforming numerous baselines.
• For the Natural Questions dataset, RocketQAv2 maintained competitive performance with Recall@100 reaching 89.

Practical and Theoretical Implications

Practically, RocketQAv2 represents a significant step towards more efficient and accurate information retrieval systems. Its dynamic and unified training method provides a streamlined approach that could reduce training time and computational costs while enhancing output quality. Theoretically, the paper advances our understanding of joint optimization in NLP tasks, suggesting potential for further exploration into similar dynamic training strategies across various machine learning applications.

Future Directions

The research opens avenues for further developments in AI, particularly:

• Extending joint training methods to multi-language or multi-domain datasets.
• Investigating variations of dynamic distillation to enhance model interpretability and robustness.
• Exploring the scalability of this joint training framework with even larger datasets and more complex retrieval scenarios.

Overall, the RocketQAv2 methodology presents a comprehensive and effective approach to bridge the gap between dense retrieval and re-ranking processes, setting a precedent for future research in efficient NLP systems.