Pretrained Transformers for Text Ranking: BERT and Beyond

Abstract: The goal of text ranking is to generate an ordered list of texts retrieved from a corpus in response to a query. Although the most common formulation of text ranking is search, instances of the task can also be found in many natural language processing applications. This survey provides an overview of text ranking with neural network architectures known as transformers, of which BERT is the best-known example. The combination of transformers and self-supervised pretraining has been responsible for a paradigm shift in NLP, information retrieval (IR), and beyond. In this survey, we provide a synthesis of existing work as a single point of entry for practitioners who wish to gain a better understanding of how to apply transformers to text ranking problems and researchers who wish to pursue work in this area. We cover a wide range of modern techniques, grouped into two high-level categories: transformer models that perform reranking in multi-stage architectures and dense retrieval techniques that perform ranking directly. There are two themes that pervade our survey: techniques for handling long documents, beyond typical sentence-by-sentence processing in NLP, and techniques for addressing the tradeoff between effectiveness (i.e., result quality) and efficiency (e.g., query latency, model and index size). Although transformer architectures and pretraining techniques are recent innovations, many aspects of how they are applied to text ranking are relatively well understood and represent mature techniques. However, there remain many open research questions, and thus in addition to laying out the foundations of pretrained transformers for text ranking, this survey also attempts to prognosticate where the field is heading.

• The paper presents a comprehensive survey demonstrating how BERT’s pretraining advances text ranking on benchmarks like MS MARCO.
• It details two strategies: reranking with transformers and dense retrieval via learning effective text representations to balance accuracy and efficiency.
• The study outlines future research directions including model distillation, zero-shot learning, and multilingual retrieval to expand AI-driven search.

Pretrained Transformers for Text Ranking: BERT and Beyond

The paper "Pretrained Transformers for Text Ranking: BERT and Beyond" presents a comprehensive survey of the application of transformer models, specifically BERT, to text ranking tasks. This field has witnessed significant advancements due to the paradigm shift introduced by transformers and self-supervised pretraining in NLP and information retrieval (IR).

Overview

The survey explores the impact of pretrained transformers on text ranking, distinguishing between two primary categories: transformer models for reranking in multi-stage architectures and dense retrieval techniques for direct ranking. The former involves models like BERT which excel in relevance classification, evidence aggregation, and query/document expansion. Dense retrieval leverages transformers to learn text representations, facilitating efficient nearest neighbor search.

Techniques and Approaches

Key themes include:

1. Handling Long Documents: Techniques to manage document lengths exceeding transformer input limitations. Models like Birch and CEDR aggregate information from document segments to produce effective ranking scores.
2. Effectiveness vs. Efficiency: Addressing the trade-offs between result quality and computational efficiency. Strategies involve optimizing inference costs while maintaining high retrieval performance.

Numerical Results and Claims

Strong empirical results have established transformer models as highly effective in diverse text ranking domains. For instance, the introduction of BERT demonstrated substantial improvements over pre-existing models in benchmarks like MS MARCO, marking a clear transition in the research landscape.

Implications and Future Directions

The implications of adopting pretrained transformers for text ranking are profound. Practically, they enable more accurate information retrieval across various applications, from web search to specialized domains. Theoretically, they challenge existing models by integrating sophisticated language understanding capabilities.

Moving forward, AI developments are likely to focus on:

• Enhancing model efficiency through distillation and architecture optimization.
• Exploring zero-shot and few-shot learning capabilities to reduce dependency on task-specific data.
• Expanding applicability to multilingual and multi-modal retrieval scenarios.

Conclusion

This paper synthesizes existing research, offering a starting point for both practitioners and researchers interested in transformer-based text ranking. By charting advancements "BERT and Beyond," it outlines a trajectory for continued innovation and research in AI-driven information access.