  WSD (Word Sense Disambiguation) is the task of identifying which sense of a
word is meant in a sentence or other segment of text. Researchers have worked
on this task (e.g. Pustejovsky, 2002) for years but it's still a challenging
one even for SOTA (state-of-the-art) LMs (language models). The new dataset,
TempoWiC introduced by Loureiro et al. (2022b) focuses on the fact that words
change over time. Their best baseline achieves 70.33% macro-F1. In this work,
we use two different losses simultaneously to train RoBERTa-based
classification models. We also improve our model by using another similar
dataset to generalize better. Our best configuration beats their best baseline
by 4.23% and reaches 74.56% macroF1.
