@inproceedings{rao-etal-2021-rg,
title = "{RG} {PA} at {S}em{E}val-2021 Task 1: A Contextual Attention-based Model with {R}o{BERT}a for Lexical Complexity Prediction",
author = "Rao, Gang and
Li, Maochang and
Hou, Xiaolong and
Jiang, Lianxin and
Mo, Yang and
Shen, Jianping",
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-1.79",
doi = "10.18653/v1/2021.semeval-1.79",
pages = "623--626",
abstract = "In this paper we propose a contextual attention based model with two-stage fine-tune training using RoBERTa. First, we perform the first-stage fine-tune on corpus with RoBERTa, so that the model can learn some prior domain knowledge. Then we get the contextual embedding of context words based on the token-level embedding with the fine-tuned model. And we use Kfold cross-validation to get K models and ensemble them to get the final result. Finally, we attain the 2nd place in the final evaluation phase of sub-task 2 with pearson correlation of 0.8575.",
}
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<abstract>In this paper we propose a contextual attention based model with two-stage fine-tune training using RoBERTa. First, we perform the first-stage fine-tune on corpus with RoBERTa, so that the model can learn some prior domain knowledge. Then we get the contextual embedding of context words based on the token-level embedding with the fine-tuned model. And we use Kfold cross-validation to get K models and ensemble them to get the final result. Finally, we attain the 2nd place in the final evaluation phase of sub-task 2 with pearson correlation of 0.8575.</abstract>
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%0 Conference Proceedings
%T RG PA at SemEval-2021 Task 1: A Contextual Attention-based Model with RoBERTa for Lexical Complexity Prediction
%A Rao, Gang
%A Li, Maochang
%A Hou, Xiaolong
%A Jiang, Lianxin
%A Mo, Yang
%A Shen, Jianping
%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 rao-etal-2021-rg
%X In this paper we propose a contextual attention based model with two-stage fine-tune training using RoBERTa. First, we perform the first-stage fine-tune on corpus with RoBERTa, so that the model can learn some prior domain knowledge. Then we get the contextual embedding of context words based on the token-level embedding with the fine-tuned model. And we use Kfold cross-validation to get K models and ensemble them to get the final result. Finally, we attain the 2nd place in the final evaluation phase of sub-task 2 with pearson correlation of 0.8575.
%R 10.18653/v1/2021.semeval-1.79
%U https://aclanthology.org/2021.semeval-1.79
%U https://doi.org/10.18653/v1/2021.semeval-1.79
%P 623-626
Markdown (Informal)
[RG PA at SemEval-2021 Task 1: A Contextual Attention-based Model with RoBERTa for Lexical Complexity Prediction](https://aclanthology.org/2021.semeval-1.79) (Rao et al., SemEval 2021)
ACL