An In-depth Analysis of Passage-Level Label Transfer for Contextual Document Ranking

Abstract

Pre-trained contextual language models such as BERT, GPT, and XLnet work quite well for document retrieval tasks. Such models are fine-tuned based on the query-document/query-passage level relevance labels to capture the ranking signals. However, the documents are longer than the passages and such document ranking models suffer from the token limitation (512) of BERT. Researchers proposed ranking strategies that either truncate the documents beyond the token limit or chunk the documents into units that can fit into the BERT. In the later case, the relevance labels are either directly transferred from the original query-document pair or learned through some external model. In this paper, we conduct a detailed study of the design decisions about splitting and label transfer on retrieval effectiveness and efficiency. We find that direct transfer of relevance labels from documents to passages introduces label noise that strongly affects retrieval effectiveness for large training datasets. We also find that query processing times are adversely affected by fine-grained splitting schemes. As a remedy, we propose a careful passage level labelling scheme using weak supervision that delivers improved performance (3-14% in terms of nDCG score) over most of the recently proposed models for ad-hoc retrieval while maintaining manageable computational complexity on four diverse document retrieval datasets.

Paper is about the performance analysis of contextual ranking strategies in an ad-hoc document retrieval

References in corpus (12)

• Distilling the Knowledge in a Neural Network
• Convolutional Neural Network Architectures for Matching Natural Language Sentences
• A Deep Relevance Matching Model for Ad-hoc Retrieval
• End-to-End Neural Ad-hoc Ranking with Kernel Pooling
• Training Convolutional Networks with Noisy Labels
• Deeper Text Understanding for IR with Contextual Neural Language Modeling
• Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval
• Multi-Stage Document Ranking with BERT
• Simple Applications of BERT for Ad Hoc Document Retrieval
• Pre-training Tasks for Embedding-based Large-scale Retrieval
• Sparse, Dense, and Attentional Representations for Text Retrieval
• Let's measure run time! Extending the IR replicability infrastructure to include performance aspects