2 research outputs found
Coarse-Tuning for Ad-hoc Document Retrieval Using Pre-trained Language Models
No full textFine-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.Comment: Accepted at LREC-COLING 202
