HYU at SemEval-2022 Task 2: Effective Idiomaticity Detection with Consideration at Different Levels of Contextualization

TL;DR

This paper presents a unified framework for idiomaticity detection in multi-word expressions, leveraging multi-level contextualization to improve model performance, demonstrated through extensive experiments on SemEval-2022 Task 2.

Contribution

It introduces a novel approach that considers various levels of contextual information for better idiomaticity identification, advancing prior methods.

Findings

Improved performance on SemEval-2022 Task 2

Effective use of inter- and inner-sentence context

Guidelines for future research in idiomaticity detection

Abstract

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.