HiJoNLP at SemEval-2022 Task 2: Detecting Idiomaticity of Multiword Expressions using Multilingual Pretrained Language Models
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Abstract (arXiv)
This paper describes an approach to detect idiomaticity only from the contextualized representation of a MWE over multilingual pretrained language models. Our experiments find that larger models are usually more effective in idiomaticity detection. However, using a higher layer of the model may not guarantee a better performance. In multilingual scenarios, the convergence of different languages are not consistent and rich-resource languages have big advantages over other languages.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 9 of its 19 references that are in the map. First posted on arXiv 27 May 2022.
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References · 9
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,907
- 2020Multilingual Denoising Pre-training for Neural Machine Translationcited by 1,028
- 2020Assessing Phrasal Representation and Composition in Transformerscited by 41
- 2017A Challenge Set Approach to Evaluating Machine Translationcited by 37
- 2022SemEval-2022 Task 2: Multilingual Idiomaticity Detection and Sentence Embeddingcited by 36
- 2019Still a Pain in the Neck: Evaluating Text Representations on Lexical Compositioncited by 27
- 2021AStitchInLanguageModels: Dataset and Methods for the Exploration of Idiomaticity in Pre-Trained Language Modelscited by 27
- 2018Classifying Idiomatic and Literal Expressions Using Topic Models and Intensity of Emotionscited by 13
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