@inproceedings{pan-etal-2021-deepblueai-semeval,
title = "{D}eep{B}lue{AI} at {S}em{E}val-2021 Task 1: Lexical Complexity Prediction with A Deep Ensemble Approach",
author = "Pan, Chunguang and
Song, Bingyan and
Wang, Shengguang and
Luo, Zhipeng",
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-1.72",
doi = "10.18653/v1/2021.semeval-1.72",
pages = "578--584",
abstract = "Lexical complexity plays an important role in reading comprehension. lexical complexity prediction (LCP) can not only be used as a part of Lexical Simplification systems, but also as a stand-alone application to help people better reading. This paper presents the winning system we submitted to the LCP Shared Task of SemEval 2021 that capable of dealing with both two subtasks. We first perform fine-tuning on numbers of pre-trained language models (PLMs) with various hyperparameters and different training strategies such as pseudo-labelling and data augmentation. Then an effective stacking mechanism is applied on top of the fine-tuned PLMs to obtain the final prediction. Experimental results on the Complex dataset show the validity of our method and we rank first and second for subtask 2 and 1.",
}
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<abstract>Lexical complexity plays an important role in reading comprehension. lexical complexity prediction (LCP) can not only be used as a part of Lexical Simplification systems, but also as a stand-alone application to help people better reading. This paper presents the winning system we submitted to the LCP Shared Task of SemEval 2021 that capable of dealing with both two subtasks. We first perform fine-tuning on numbers of pre-trained language models (PLMs) with various hyperparameters and different training strategies such as pseudo-labelling and data augmentation. Then an effective stacking mechanism is applied on top of the fine-tuned PLMs to obtain the final prediction. Experimental results on the Complex dataset show the validity of our method and we rank first and second for subtask 2 and 1.</abstract>
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%0 Conference Proceedings
%T DeepBlueAI at SemEval-2021 Task 1: Lexical Complexity Prediction with A Deep Ensemble Approach
%A Pan, Chunguang
%A Song, Bingyan
%A Wang, Shengguang
%A Luo, Zhipeng
%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 pan-etal-2021-deepblueai-semeval
%X Lexical complexity plays an important role in reading comprehension. lexical complexity prediction (LCP) can not only be used as a part of Lexical Simplification systems, but also as a stand-alone application to help people better reading. This paper presents the winning system we submitted to the LCP Shared Task of SemEval 2021 that capable of dealing with both two subtasks. We first perform fine-tuning on numbers of pre-trained language models (PLMs) with various hyperparameters and different training strategies such as pseudo-labelling and data augmentation. Then an effective stacking mechanism is applied on top of the fine-tuned PLMs to obtain the final prediction. Experimental results on the Complex dataset show the validity of our method and we rank first and second for subtask 2 and 1.
%R 10.18653/v1/2021.semeval-1.72
%U https://aclanthology.org/2021.semeval-1.72
%U https://doi.org/10.18653/v1/2021.semeval-1.72
%P 578-584
Markdown (Informal)
[DeepBlueAI at SemEval-2021 Task 1: Lexical Complexity Prediction with A Deep Ensemble Approach](https://aclanthology.org/2021.semeval-1.72) (Pan et al., SemEval 2021)
ACL