@inproceedings{tan-2022-hijonlp,
title = "{H}i{J}o{NLP} at {S}em{E}val-2022 Task 2: Detecting Idiomaticity of Multiword Expressions using Multilingual Pretrained Language Models",
author = "Tan, Minghuan",
editor = "Emerson, Guy and
Schluter, Natalie and
Stanovsky, Gabriel and
Kumar, Ritesh and
Palmer, Alexis and
Schneider, Nathan and
Singh, Siddharth and
Ratan, Shyam",
booktitle = "Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.semeval-1.23",
doi = "10.18653/v1/2022.semeval-1.23",
pages = "190--196",
abstract = "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.",
}
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<abstract>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.</abstract>
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%0 Conference Proceedings
%T HiJoNLP at SemEval-2022 Task 2: Detecting Idiomaticity of Multiword Expressions using Multilingual Pretrained Language Models
%A Tan, Minghuan
%Y Emerson, Guy
%Y Schluter, Natalie
%Y Stanovsky, Gabriel
%Y Kumar, Ritesh
%Y Palmer, Alexis
%Y Schneider, Nathan
%Y Singh, Siddharth
%Y Ratan, Shyam
%S Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, United States
%F tan-2022-hijonlp
%X 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.
%R 10.18653/v1/2022.semeval-1.23
%U https://aclanthology.org/2022.semeval-1.23
%U https://doi.org/10.18653/v1/2022.semeval-1.23
%P 190-196
Markdown (Informal)
[HiJoNLP at SemEval-2022 Task 2: Detecting Idiomaticity of Multiword Expressions using Multilingual Pretrained Language Models](https://aclanthology.org/2022.semeval-1.23) (Tan, SemEval 2022)
ACL