Adversarial Retriever-Ranker for dense text retrieval (2110.03611v5)

Abstract: Current dense text retrieval models face two typical challenges. First, they adopt a siamese dual-encoder architecture to encode queries and documents independently for fast indexing and searching, while neglecting the finer-grained term-wise interactions. This results in a sub-optimal recall performance. Second, their model training highly relies on a negative sampling technique to build up the negative documents in their contrastive losses. To address these challenges, we present Adversarial Retriever-Ranker (AR2), which consists of a dual-encoder retriever plus a cross-encoder ranker. The two models are jointly optimized according to a minimax adversarial objective: the retriever learns to retrieve negative documents to cheat the ranker, while the ranker learns to rank a collection of candidates including both the ground-truth and the retrieved ones, as well as providing progressive direct feedback to the dual-encoder retriever. Through this adversarial game, the retriever gradually produces harder negative documents to train a better ranker, whereas the cross-encoder ranker provides progressive feedback to improve retriever. We evaluate AR2 on three benchmarks. Experimental results show that AR2 consistently and significantly outperforms existing dense retriever methods and achieves new state-of-the-art results on all of them. This includes the improvements on Natural Questions R@5 to 77.9%(+2.1%), TriviaQA R@5 to 78.2%(+1.4), and MS-MARCO MRR@10 to 39.5%(+1.3%). Code and models are available at https://github.com/microsoft/AR2.

Adversarial Retriever-Ranker for Dense Text Retrieval

The paper "Adversarial Retriever-Ranker for Dense Text Retrieval" proposes an innovative framework named Adversarial Retriever-Ranker (AR2) aimed at enhancing the performance of dense text retrieval systems. This work specifically addresses shortcomings identified in traditional dense retrieval models, particularly the inadequate model of fine-grained interactions between queries and documents due to their reliance on a siamese dual-encoder architecture, and the inefficiencies stemming from negative sampling techniques.

Key Contributions

1. Novel Architecture: The AR2 framework comprises two modules: a dual-encoder retriever and a cross-encoder ranker. In contrast to standalone dual-encoder models, AR2 incorporates comprehensive interaction modeling through a ranker that combines query and document, facilitating improved relevance scoring.
2. Adversarial Training Objective: AR2 employs a minimax adversarial objective that incorporates an iterative learning process. In this process, the retriever is optimized to produce challenging negative samples to confuse the ranker, while the ranker is simultaneously trained to accurately distinguish between correct and adversarially generated samples. This interactive approach not only improves the retriever's ability to handle "hard negatives" effectively but also enhances the ranker's robustness.
3. Experimental Evaluation: The AR2 framework was rigorously evaluated on three standard benchmarks: Natural Questions, TriviaQA, and MS-MARCO. AR2 achieved state-of-the-art results on each, demonstrating notable improvements over existing methods. For instance, improvements in retrieval recall exhibited enhancements up to 2.1% on R@5 for the Natural Questions dataset, showcasing the effectiveness of the adversarial training paradigm.
4. Distillation Regularization Strategy: To prevent premature convergence of the retriever to a sharply peaked probability distribution, the authors incorporate a knowledge distillation regularization term. This strategy enforces smoother probability distributions and augments the learning process, ensuring diverse exploration during training.

Theoretical and Practical Implications

The theoretical implications of this research are significant, as AR2 challenges current paradigms in dense retrieval by blending generative adversarial approaches with classical IR techniques. Practically, the systematic integration of a ranker that leverages self-attention mechanisms across concatenated queries and documents sets a precedent for future work to explore cross-encoder architectures in dense retrieval. Moreover, the improvements observed in benchmark tests suggest substantial potential for AR2 to enhance real-world applications such as search engines and open-domain question answering systems.

Speculation on Future Developments

Looking forward, the AR2 framework could be pivotal in guiding new research directions within AI-driven text retrieval. Future work may explore various facets of adversarial training, such as improving the robustness of rankers against deliberate noise and optimizing computational costs associated with cross-encoder models. The development of more sophisticated feedback mechanisms within the retriever-ranker interaction could further refine the quality of retrieval. Additionally, enhancements in the scalability of AR2 could make it a viable solution for larger, more diverse datasets, broadening its applicability in industrial settings.

This work presents a substantial advancement in dense text retrieval technology, with its adversarial approach likely to spark ongoing innovation in methods that demand efficient and effective retrieval solutions.