Coarse-Tuning for Ad-hoc Document Retrieval Using Pre-trained Language Models

• URL: http://arxiv.org/abs/2403.16915v3
• Date: Wed, 27 Mar 2024 01:53:36 GMT
• Title: Coarse-Tuning for Ad-hoc Document Retrieval Using Pre-trained Language Models
• Authors: Atsushi Keyaki, Ribeka Keyaki,
• Abstract summary: Fine-tuning in information retrieval systems requires learning query representations and query-document relations. This study introduces coarse-tuning as an intermediate learning stage that bridges pre-training and fine-tuning. We propose Query-Document Pair Prediction (QDPP) for coarse-tuning, which predicts the appropriateness of query-document pairs.
• Score: 1.7126893619099555
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Fine-tuning in information retrieval systems using pre-trained language models (PLM-based IR) requires learning query representations and query-document relations, in addition to downstream task-specific learning. This study introduces coarse-tuning as an intermediate learning stage that bridges pre-training and fine-tuning. By learning query representations and query-document relations in coarse-tuning, we aim to reduce the load of fine-tuning and improve the learning effect of downstream IR tasks. We propose Query-Document Pair Prediction (QDPP) for coarse-tuning, which predicts the appropriateness of query-document pairs. Evaluation experiments show that the proposed method significantly improves MRR and/or nDCG@5 in four ad-hoc document retrieval datasets. Furthermore, the results of the query prediction task suggested that coarse-tuning facilitated learning of query representation and query-document relations.

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