@inproceedings{qiu-etal-2021-learning,
title = "Learning Syntactic Dense Embedding with Correlation Graph for Automatic Readability Assessment",
author = "Qiu, Xinying and
Chen, Yuan and
Chen, Hanwu and
Nie, Jian-Yun and
Shen, Yuming and
Lu, Dawei",
editor = "Zong, Chengqing and
Xia, Fei and
Li, Wenjie and
Navigli, Roberto",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.235",
doi = "10.18653/v1/2021.acl-long.235",
pages = "3013--3025",
abstract = "Deep learning models for automatic readability assessment generally discard linguistic features traditionally used in machine learning models for the task. We propose to incorporate linguistic features into neural network models by learning syntactic dense embeddings based on linguistic features. To cope with the relationships between the features, we form a correlation graph among features and use it to learn their embeddings so that similar features will be represented by similar embeddings. Experiments with six data sets of two proficiency levels demonstrate that our proposed methodology can complement BERT-only model to achieve significantly better performances for automatic readability assessment.",
}
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<abstract>Deep learning models for automatic readability assessment generally discard linguistic features traditionally used in machine learning models for the task. We propose to incorporate linguistic features into neural network models by learning syntactic dense embeddings based on linguistic features. To cope with the relationships between the features, we form a correlation graph among features and use it to learn their embeddings so that similar features will be represented by similar embeddings. Experiments with six data sets of two proficiency levels demonstrate that our proposed methodology can complement BERT-only model to achieve significantly better performances for automatic readability assessment.</abstract>
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%0 Conference Proceedings
%T Learning Syntactic Dense Embedding with Correlation Graph for Automatic Readability Assessment
%A Qiu, Xinying
%A Chen, Yuan
%A Chen, Hanwu
%A Nie, Jian-Yun
%A Shen, Yuming
%A Lu, Dawei
%Y Zong, Chengqing
%Y Xia, Fei
%Y Li, Wenjie
%Y Navigli, Roberto
%S Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F qiu-etal-2021-learning
%X Deep learning models for automatic readability assessment generally discard linguistic features traditionally used in machine learning models for the task. We propose to incorporate linguistic features into neural network models by learning syntactic dense embeddings based on linguistic features. To cope with the relationships between the features, we form a correlation graph among features and use it to learn their embeddings so that similar features will be represented by similar embeddings. Experiments with six data sets of two proficiency levels demonstrate that our proposed methodology can complement BERT-only model to achieve significantly better performances for automatic readability assessment.
%R 10.18653/v1/2021.acl-long.235
%U https://aclanthology.org/2021.acl-long.235
%U https://doi.org/10.18653/v1/2021.acl-long.235
%P 3013-3025
Markdown (Informal)
[Learning Syntactic Dense Embedding with Correlation Graph for Automatic Readability Assessment](https://aclanthology.org/2021.acl-long.235) (Qiu et al., ACL-IJCNLP 2021)
ACL