Multi-Stage Document Ranking with BERT (1910.14424v1)

Abstract: The advent of deep neural networks pre-trained via LLMing tasks has spurred a number of successful applications in natural language processing. This work explores one such popular model, BERT, in the context of document ranking. We propose two variants, called monoBERT and duoBERT, that formulate the ranking problem as pointwise and pairwise classification, respectively. These two models are arranged in a multi-stage ranking architecture to form an end-to-end search system. One major advantage of this design is the ability to trade off quality against latency by controlling the admission of candidates into each pipeline stage, and by doing so, we are able to find operating points that offer a good balance between these two competing metrics. On two large-scale datasets, MS MARCO and TREC CAR, experiments show that our model produces results that are either at or comparable to the state of the art. Ablation studies show the contributions of each component and characterize the latency/quality tradeoff space.

Overview of Multi-Stage Document Ranking with BERT

The paper entitled "Multi-Stage Document Ranking with BERT" tackles the problem of document retrieval, a crucial task in Information Retrieval (IR), by employing the Bidirectional Encoder Representations from Transformers (BERT) model in an advanced multi-stage ranking architecture. The work presents two variants of BERT models, namely monoBERT and duoBERT, and integrates them into a framework that balances retrieval quality against computational latency.

The Multi-Stage Framework

The proposed architecture comprises multiple stages, each designed to progressively refine a set of candidate documents to maximize retrieval performance. The initial stage ($H_0$) involves a traditional retrieval approach using BM25, a popular scoring function that treats user queries as "bags of words." This stage aims to ensure high recall by retrieving a comprehensive set of candidates.

The subsequent stages employ the BERT models. The monoBERT model ($H_1$) treats document ranking as a binary classification task, assessing the relevance of each document to the query in isolation. Meanwhile, duoBERT ($H_2$) addresses ranking as a pairwise classification problem, comparing pairs of documents to ascertain their relative relevance concerning the query.

Evaluation and Results

The effectiveness of the proposed architecture is evaluated on two substantial datasets: MS MARCO and TREC CAR. For MS MARCO, the authors achieve results that are competitive with or exceed state-of-the-art methods, with monoBERT providing a notable improvement over traditional BM25 alone. The duoBERT model further enhances performance by optimizing pairwise comparisons, detailed through various aggregation methods to determine relevance scores effectively. The authors also investigate the impact of pre-training on the target corpus (TCP), which demonstrates additional improvements over initial BERT pre-training using out-of-domain corpora.

A pivotal contribution is the exploration of latency versus quality trade-offs. By varying the number of candidates and adjusting the architecture's parameters, the research delineates how quality improvements come with an increased computational cost, facilitating practical deployments in real-world settings.

Theoretical and Practical Implications

Theoretically, this research advances understanding in employing BERT for context-sensitive document retrieval by effectively leveraging its contextual embeddings in ranking tasks. The paper confirms that pre-trained models like BERT can significantly enhance retrieval performance even when applied to downstream tasks such as document ranking.

Practically, the paper reveals how BERT's deployment can be optimized by controlling latency, making these models more suitable for real-time search applications. The findings underscore that careful design of search architecture, particularly through multi-stage ranking strategies, can yield substantial improvements without incurring prohibitive computational costs.

Considerations for Future AI Research

Future research may delve into joint training across pipeline stages or incorporate explicit scoring signals from earlier stages to optimize end-to-end performance further. Exploring models capable of handling larger document inputs could also extend these findings, particularly relevant to tasks involving more extensive document datasets.

As AI models continue to evolve, the insights gleaned from this work can serve as a foundation for developing more sophisticated retrieval systems, blending high-quality results with manageable computational demands. The integration of advanced LLMs like BERT within established retrieval frameworks continues to be a promising avenue for achieving efficient and effective information retrieval.