TempoWiC: An Evaluation Benchmark for Detecting Meaning Shift in Social Media
Daniel Loureiro, Aminette D'Souza, Areej Nasser Muhajab, Isabella A. White, Gabriel Wong, Luis Espinosa Anke, Leonardo Neves, Francesco Barbieri, Jose Camacho-Collados
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Abstract
Language evolves over time, and word meaning changes accordingly. This is especially true in social media, since its dynamic nature leads to faster semantic shifts, making it challenging for NLP models to deal with new content and trends. However, the number of datasets and models that specifically address the dynamic nature of these social platforms is scarce. To bridge this gap, we present TempoWiC, a new benchmark especially aimed at accelerating research in social media-based meaning shift. Our results show that TempoWiC is a challenging benchmark, even for recently-released language models specialized in social media.