HYU at SemEval-2022 Task 2: Effective Idiomaticity Detection with Consideration at Different Levels of Contextualization
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 6 of its 19 references that are in the map. First posted on arXiv 1 Jun 2022.
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References · 6
- 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,935
- 2021MelBERT: Metaphor Detection via Contextualized Late Interaction using Metaphorical Identification Theoriescited by 46
- 2022SemEval-2022 Task 2: Multilingual Idiomaticity Detection and Sentence Embeddingcited by 36
- 2018Neural Metaphor Detection in Contextcited by 34
- 2021AStitchInLanguageModels: Dataset and Methods for the Exploration of Idiomaticity in Pre-Trained Language Modelscited by 27
- 2021Idiomatic Expression Identification using Semantic Compatibilitycited by 19
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