A fundamental challenge in the current NLP context, dominated by language models, comes from the inflexibility of current architectures to 'learn' new information. While model-centric solutions like continual learning or parameter-efficient fine tuning are available, the question still remains of how to reliably identify changes in language or in the world. In this paper, we propose WikiTiDe, a dataset derived from pairs of timestamped definitions extracted from Wikipedia. We argue that such resource can be helpful for accelerating diachronic NLP, specifically, for training models able to scan knowledge resources for core updates concerning a concept, an event, or a named entity. Our proposed end-to-end method is fully automatic, and leverages a bootstrapping algorithm for gradually creating a high-quality dataset. Our results suggest that bootstrapping the seed version of WikiTiDe leads to better fine-tuned models. We also leverage fine-tuned models in a number of downstream tasks, showing promising results with respect to competitive baselines.

本文介绍了WikiTiDe数据集，该数据集通过从维基百科提取的带时间戳的定义对创建而来。我们提出了完全自动的端到端方法，并利用自举算法逐步创建高质量数据集。研究结果表明，自举种子版本的WikiTiDe可以得到更好的微调模型，并在多个下游任务中取得了有竞争力的结果。

WIKITIDE: 基于维基百科的时间戳定义对数据集