HYU at SemEval-2022 Task 2: Effective Idiomaticity Detection with Consideration at Different Levels of Contextualization
This work addresses a specific challenge in natural language processing for researchers and practitioners, but it is incremental as it builds on existing methods for a known task.
The paper tackles the problem of detecting idiomaticity in multi-word expressions by proposing a unified framework that considers contextualization at different levels, achieving improved performance in related models.
We propose a unified framework that enables us to consider various aspects of contextualization at different levels to better identify the idiomaticity of multi-word expressions. Through extensive experiments, we demonstrate that our approach based on the inter- and inner-sentence context of a target MWE is effective in improving the performance of related models. We also share our experience in detail on the task of SemEval-2022 Tasks 2 such that future work on the same task can be benefited from this.