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arXiv · 2103.03335

A Systematic Evaluation of Transfer Learning and Pseudo-labeling with BERT-based Ranking Models

Iurii Mokrii·Leonid Boytsov·Pavel Braslavski
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Abstract

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.

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Iurii Mokrii, Leonid Boytsov, Pavel Braslavski. 2021-03-04. A Systematic Evaluation of Transfer Learning and Pseudo-labeling with BERT-based Ranking Models. https://doi.org/10.1145/3404835.3463093

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