MLLabs-LIG at TempoWiC 2022: A Generative Approach for Examining Temporal Meaning Shift
Chenyang Lyu, Yongxin Zhou, Tianbo Ji · 2022
In this paper, we present our system for the EvoNLP 2022 shared task Temporal Meaning Shift (TempoWiC).Different from the typically used discriminative model, we propose a generative approach based on pre-trained generation models.The basic architecture of our system is a seq2seq model where the input sequence consists of two documents followed by a question asking whether the meaning of target word changed or not, the target output sequence is a declarative sentence describing the meaning of target word changed or not.The experimental results on TempoWiC test set show that our best system (with time information) obtained an accuracy and Macro-F1 score of 68.09% and 62.59% respectively, which ranked 12th among all submitted systems.The results have shown the plausibility of using generation model for WiC tasks, meanwhile also indicate there's still room for further improvement.