Proceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia P 2023
DOI: 10.1145/3624918.3625333
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Annotating Data for Fine-Tuning a Neural Ranker? Current Active Learning Strategies are not Better than Random Selection

Sophia Althammer,
Guido Zuccon,
Sebastian Hofstätter
et al.

Abstract: Search methods based on Pretrained Language Models (PLM) have demonstrated great effectiveness gains compared to statistical and early neural ranking models. However, fine-tuning PLM-based rankers requires a great amount of annotated training data. Annotating data involves a large manual effort and thus is expensive, especially in domain specific tasks. In this paper we investigate finetuning PLM-based rankers under limited training data and budget. We investigate two scenarios: fine-tuning a ranker from scrat… Show more

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