Paper Reading AI Learner

Metadata Might Make Language Models Better

2022-11-18 08:29:00
Kaspar Beelen, Daniel van Strien

Abstract

This paper discusses the benefits of including metadata when training language models on historical collections. Using 19th-century newspapers as a case study, we extend the time-masking approach proposed by Rosin et al., 2022 and compare different strategies for inserting temporal, political and geographical information into a Masked Language Model. After fine-tuning several DistilBERT on enhanced input data, we provide a systematic evaluation of these models on a set of evaluation tasks: pseudo-perplexity, metadata mask-filling and supervised classification. We find that showing relevant metadata to a language model has a beneficial impact and may even produce more robust and fairer models.

Abstract (translated)

URL

https://arxiv.org/abs/2211.10086

PDF

https://arxiv.org/pdf/2211.10086.pdf


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