@inproceedings{king-etal-2021-unbnlp,
title = "{UNBNLP} at {S}em{E}val-2021 Task 1: Predicting lexical complexity with masked language models and character-level encoders",
author = "King, Milton and
Hakimi Parizi, Ali and
Fakharian, Samin and
Cook, Paul",
editor = "Palmer, Alexis and
Schneider, Nathan and
Schluter, Natalie and
Emerson, Guy and
Herbelot, Aurelie and
Zhu, Xiaodan",
booktitle = "Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.semeval-1.83",
doi = "10.18653/v1/2021.semeval-1.83",
pages = "650--654",
abstract = "In this paper, we present three supervised systems for English lexical complexity prediction of single and multiword expressions for SemEval-2021 Task 1. We explore the use of statistical baseline features, masked language models, and character-level encoders to predict the complexity of a target token in context. Our best system combines information from these three sources. The results indicate that information from masked language models and character-level encoders can be combined to improve lexical complexity prediction.",
}
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<abstract>In this paper, we present three supervised systems for English lexical complexity prediction of single and multiword expressions for SemEval-2021 Task 1. We explore the use of statistical baseline features, masked language models, and character-level encoders to predict the complexity of a target token in context. Our best system combines information from these three sources. The results indicate that information from masked language models and character-level encoders can be combined to improve lexical complexity prediction.</abstract>
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%0 Conference Proceedings
%T UNBNLP at SemEval-2021 Task 1: Predicting lexical complexity with masked language models and character-level encoders
%A King, Milton
%A Hakimi Parizi, Ali
%A Fakharian, Samin
%A Cook, Paul
%Y Palmer, Alexis
%Y Schneider, Nathan
%Y Schluter, Natalie
%Y Emerson, Guy
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%S Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F king-etal-2021-unbnlp
%X In this paper, we present three supervised systems for English lexical complexity prediction of single and multiword expressions for SemEval-2021 Task 1. We explore the use of statistical baseline features, masked language models, and character-level encoders to predict the complexity of a target token in context. Our best system combines information from these three sources. The results indicate that information from masked language models and character-level encoders can be combined to improve lexical complexity prediction.
%R 10.18653/v1/2021.semeval-1.83
%U https://aclanthology.org/2021.semeval-1.83
%U https://doi.org/10.18653/v1/2021.semeval-1.83
%P 650-654
Markdown (Informal)
[UNBNLP at SemEval-2021 Task 1: Predicting lexical complexity with masked language models and character-level encoders](https://aclanthology.org/2021.semeval-1.83) (King et al., SemEval 2021)
ACL