HYU at S em E val-2022 Task 2: Effective Idiomaticity Detection with Consideration at Different Levels of Contextualization (2022)

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.

## introduction multi-word expressions (mwes) are a group of linguistic components containing two or more words with outstanding collocation @xcite . mwes are valuable in that they contribute to enriching the expressiveness of a language, allowing diverse interpretations of their meaning according to the context in which they are located. that is, the semantics of an mwe can be originated from either (i) the direct composition of the literal definitions of its constituents (i.e., compositional meaning) or (ii) its conventional usage in the language (i.e., idiomatic meaning). for instance, given an expression called wet blanket, its compositional meaning is 'a piece of cloth soaked in liquid', whereas its idiomatic meaning is 'a person who spoils the mood' (see while mwes function as an effective means of improving the abundance of a language, they are also one of the main obstacles that complicate natural language processing (nlp), from the perspective that an nlp model should be able to precisely identify their mode. in addition, the current trend where the goal of most nlp models is chiefly focused on capturing compositionality raises the question of how properly to deal with ## proposed method as a participant of semeval-2022 task 2, we propose a framework powered on four features devised to facilitate the detection of idiomatic expressions. these features are computed by the same foundation model, foot_2 but distinguished from each other by what is inserted into the model as input to compute the features. a simple linear classifier is introduced on top of the concatenation of the four features to finally gauge the idiomaticity of an mwe in an input sequence. figure 1 presents the overall picture of our method. ## conclusion in this work, we investigate the method of implementing better idiomaticity detection models by considering different levels of contextualization. we propose four features grounded on the surrounding and inner-sentence context of a target mwe, showing that these features are effective in improving performance. moreover, we present a discussion on the issue related to the validation set in the one-shot setting and the impact of the form of mwes. as future work, we plan to develop a method of designing the interaction between related features in a more sophisticated fashion, instead of simply concatenating them.