PARADE: Passage Representation Aggregation for Document Reranking (2008.09093v2)

Abstract: Pretrained transformer models, such as BERT and T5, have shown to be highly effective at ad-hoc passage and document ranking. Due to inherent sequence length limits of these models, they need to be run over a document's passages, rather than processing the entire document sequence at once. Although several approaches for aggregating passage-level signals have been proposed, there has yet to be an extensive comparison of these techniques. In this work, we explore strategies for aggregating relevance signals from a document's passages into a final ranking score. We find that passage representation aggregation techniques can significantly improve over techniques proposed in prior work, such as taking the maximum passage score. We call this new approach PARADE. In particular, PARADE can significantly improve results on collections with broad information needs where relevance signals can be spread throughout the document (such as TREC Robust04 and GOV2). Meanwhile, less complex aggregation techniques may work better on collections with an information need that can often be pinpointed to a single passage (such as TREC DL and TREC Genomics). We also conduct efficiency analyses, and highlight several strategies for improving transformer-based aggregation.

Passage Representation Aggregation for Document Reranking: Exploring PARADE

This academic paper presents a paper on different methods of aggregating passage-level relevance signals to enhance document reranking performance in information retrieval tasks. The proposed model, PARADE (Passage Representation Aggregation for Document Reranking), aims to improve upon existing transformer models, such as BERT and T5, which face limitations due to maximum sequence length constraints. The paper establishes that effective aggregation of passage representation, rather than relying solely on maximum passage scoring techniques, contributes to a more accurate relevance estimation, particularly in document collections where relevance signals are distributed throughout the text.

Key Contributions

1. Formalization of Aggregation Strategies: The authors formalize techniques for aggregating passage relevance scores and representations, demonstrating the potential of end-to-end training for each strategy. They propose various techniques within PARADE, including sum pooling, max pooling, average pooling, attention-based pooling, CNNs, and transformer-based representation aggregation.
2. Comprehensive Benchmark Comparisons: The paper provides an exhaustive comparison of passage aggregation techniques across multiple benchmark datasets. The results substantiate the efficacy of passage representation aggregation methods, particularly on datasets like TREC Robust04 and GOV2, where the relevance is dispersed throughout the document.
3. Efficiency Analysis: The researchers examine the computational requirements of PARADE, noting that while representation aggregation might increase model complexity, its performance gains often justify the added costs. They propose efficiency improvements through knowledge distillation, resulting in smaller yet effective models.
4. Data Characteristics and Strategy Suitability: An analytic view is provided on why certain aggregation strategies, such as simpler max-pooling, might outperform others like PARADE-Transformer on certain datasets characterized by concentrated relevance signals, such as the TREC DL and Genomics collections.

Empirical Insights

• Effectiveness: On collections characterized by pervasive information needs, PARADE models, especially with CNN and transformer aggregators, outperform traditional max-pooling approaches, yielding substantial improvements in performance metrics like MAP and nDCG@20.
• Transformers for Aggregation: Architectures with CNNs and transformers for representation aggregation show superior performance, illustrating the importance of integrating positional and content-based cross-passage signals.
• Reduction in Computational Loads: Knowledge distillation methods can substantially reduce model size and inference time while retaining significant effectiveness, making PARADE practical for real-time document reranking applications.

Implications and Future Outlook

This research outlines a promising direction for future advancements in AI-driven information retrieval systems. The key implication is the necessity of refined aggregation techniques to exploit document-wide contextual cues effectively. As AI models become more efficient, further developments in transformer architectures tailored to handle longer sequences directly without needing passage splits are anticipated. These advances will likely continue to enhance the ability of IR systems to adequately meet complex information needs in diverse domains, from general web searches to specialized academic research.

Overall, the PARADE approach represents an engaging step towards optimizing document reranking by leveraging sophisticated neural aggregation mechanisms, aligning with the ongoing progression towards more contextually aware information retrieval models.