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

• URL: http://arxiv.org/abs/2103.03335v1
• Date: Thu, 4 Mar 2021 21:08:06 GMT
• Title: A Systematic Evaluation of Transfer Learning and Pseudo-labeling with BERT-based Ranking Models
• Authors: Iurii Mokrii, Leonid Boytsov, Pavel Braslavski
• Abstract summary: We evaluate transferability of BERT-based neural ranking models across five English datasets. Each of our collections has a substantial number of queries, which enables a full-shot evaluation mode. We find that training on pseudo-labels can produce a competitive or better model compared to transfer learning.
• Score: 2.0498977512661267
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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. However, there is a need to improve the stability and/or effectiveness of the few-shot training, which, in some cases, can degrade performance of a pretrained model.

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