A Systematic Evaluation of Transfer Learning and Pseudo-labeling with BERT-based Ranking Models
This addresses the problem of high annotation costs for information retrieval researchers by comparing transfer learning and pseudo-labeling methods, but it is incremental as it builds on existing BERT-based models and datasets.
The study systematically evaluated transfer learning and pseudo-labeling with BERT-based ranking models across five English datasets, finding that training on pseudo-labels, sometimes with fine-tuning, can produce competitive or better models compared to transfer learning, though few-shot training stability needs improvement.
Due to high annotation costs making the best use of existing human-created training data is an important research direction. We, therefore, carry out a systematic evaluation of transferability of BERT-based neural ranking models across five English datasets. Previous studies focused primarily on zero-shot and few-shot transfer from a large dataset to a dataset with a small number of queries. In contrast, each of our collections has a substantial number of queries, which enables a full-shot evaluation mode and improves reliability of our results. Furthermore, since source datasets licences often prohibit commercial use, we compare transfer learning to training on pseudo-labels generated by a BM25 scorer. We find that training on pseudo-labels -- possibly with subsequent fine-tuning using a modest number of annotated queries -- can produce a competitive or better model compared to transfer learning. Yet, it is necessary to improve the stability and/or effectiveness of the few-shot training, which, sometimes, can degrade performance of a pretrained model.