Enhancing Detailed Feedback to Chinese Writing Learners Using a Soft-Label Driven Approach and Tag-Aware Ranking Model

Yuzhe Cai; Shaoguang Mao; Chenshuo Wang; Tao Ge; Wenshan Wu; Yan Xia; Chanjin Zheng; Qiang Guan

doi:10.1007/978-3-031-44693-1_45

Enhancing Detailed Feedback to Chinese Writing Learners Using a Soft-Label Driven Approach and Tag-Aware Ranking Model

Yuzhe Cai
, Shaoguang Mao^*
, Chenshuo Wang
, Tao Ge
, Wenshan Wu
, Yan Xia
, Chanjin Zheng
, Qiang Guan

^*Corresponding author for this work

Faculty of Education

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

This paper focuses on providing detailed and specific feedback for Chinese writing learners, which is challenging due to the uncertainty of the feedback space. 30 common tags are identified based on clustering 363k comment phrases. By predicting the corresponding tags for an input, learners can gain insights on how to improve their work. However, the various tag types and non-exhaustive annotation pose challenges for model training. To address this, we propose a soft-label-driven approach to construct a more accurate relationship between samples and the tag space. We use a relevance matrix between different tags to adjust the positive label’s confidence across the entire tag space. Additionally, we propose a tag-aware regression-based ranking model that further improves performance. Our experiments demonstrate that the proposed soft-label approach performs better than a hard-label approach, and the proposed ranking model enhances performance. Our data and code are available at: https://github.com/Zhe0311/DetailedFeedback2ChineseWritingLearners.

Original language	English
Title of host publication	Natural Language Processing and Chinese Computing - 12th National CCF Conference, NLPCC 2023, Proceedings
Editors	Fei Liu, Nan Duan, Qingting Xu, Yu Hong
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	576-587
Number of pages	12
ISBN (Print)	9783031446924
DOIs	https://doi.org/10.1007/978-3-031-44693-1_45
State	Published - 2023
Event	12th National CCF Conference on Natural Language Processing and Chinese Computing, NLPCC 2023 - Foshan, China Duration: 12 Oct 2023 → 15 Oct 2023

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	14302 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	12th National CCF Conference on Natural Language Processing and Chinese Computing, NLPCC 2023
Country/Territory	China
City	Foshan
Period	12/10/23 → 15/10/23

Keywords

Automated feedback
Computer-aided language learning
Essay assessment
Soft-label tag-aware ranking model

Access to Document

10.1007/978-3-031-44693-1_45

Cite this

Cai, Y., Mao, S., Wang, C., Ge, T., Wu, W., Xia, Y., Zheng, C., & Guan, Q. (2023). Enhancing Detailed Feedback to Chinese Writing Learners Using a Soft-Label Driven Approach and Tag-Aware Ranking Model. In F. Liu, N. Duan, Q. Xu, & Y. Hong (Eds.), Natural Language Processing and Chinese Computing - 12th National CCF Conference, NLPCC 2023, Proceedings (pp. 576-587). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14302 LNAI). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-44693-1_45

Cai, Yuzhe ; Mao, Shaoguang ; Wang, Chenshuo et al. / Enhancing Detailed Feedback to Chinese Writing Learners Using a Soft-Label Driven Approach and Tag-Aware Ranking Model. Natural Language Processing and Chinese Computing - 12th National CCF Conference, NLPCC 2023, Proceedings. editor / Fei Liu ; Nan Duan ; Qingting Xu ; Yu Hong. Springer Science and Business Media Deutschland GmbH, 2023. pp. 576-587 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{e21e135825cd4a14b5f0b49cded8a640,

title = "Enhancing Detailed Feedback to Chinese Writing Learners Using a Soft-Label Driven Approach and Tag-Aware Ranking Model",

abstract = "This paper focuses on providing detailed and specific feedback for Chinese writing learners, which is challenging due to the uncertainty of the feedback space. 30 common tags are identified based on clustering 363k comment phrases. By predicting the corresponding tags for an input, learners can gain insights on how to improve their work. However, the various tag types and non-exhaustive annotation pose challenges for model training. To address this, we propose a soft-label-driven approach to construct a more accurate relationship between samples and the tag space. We use a relevance matrix between different tags to adjust the positive label{\textquoteright}s confidence across the entire tag space. Additionally, we propose a tag-aware regression-based ranking model that further improves performance. Our experiments demonstrate that the proposed soft-label approach performs better than a hard-label approach, and the proposed ranking model enhances performance. Our data and code are available at: https://github.com/Zhe0311/DetailedFeedback2ChineseWritingLearners.",

keywords = "Automated feedback, Computer-aided language learning, Essay assessment, Soft-label tag-aware ranking model",

author = "Yuzhe Cai and Shaoguang Mao and Chenshuo Wang and Tao Ge and Wenshan Wu and Yan Xia and Chanjin Zheng and Qiang Guan",

note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 12th National CCF Conference on Natural Language Processing and Chinese Computing, NLPCC 2023 ; Conference date: 12-10-2023 Through 15-10-2023",

year = "2023",

doi = "10.1007/978-3-031-44693-1\_45",

language = "英语",

isbn = "9783031446924",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "576--587",

editor = "Fei Liu and Nan Duan and Qingting Xu and Yu Hong",

booktitle = "Natural Language Processing and Chinese Computing - 12th National CCF Conference, NLPCC 2023, Proceedings",

address = "德国",

}

Cai, Y, Mao, S, Wang, C, Ge, T, Wu, W, Xia, Y, Zheng, C & Guan, Q 2023, Enhancing Detailed Feedback to Chinese Writing Learners Using a Soft-Label Driven Approach and Tag-Aware Ranking Model. in F Liu, N Duan, Q Xu & Y Hong (eds), Natural Language Processing and Chinese Computing - 12th National CCF Conference, NLPCC 2023, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 14302 LNAI, Springer Science and Business Media Deutschland GmbH, pp. 576-587, 12th National CCF Conference on Natural Language Processing and Chinese Computing, NLPCC 2023, Foshan, China, 12/10/23. https://doi.org/10.1007/978-3-031-44693-1_45

Enhancing Detailed Feedback to Chinese Writing Learners Using a Soft-Label Driven Approach and Tag-Aware Ranking Model. / Cai, Yuzhe; Mao, Shaoguang; Wang, Chenshuo et al.
Natural Language Processing and Chinese Computing - 12th National CCF Conference, NLPCC 2023, Proceedings. ed. / Fei Liu; Nan Duan; Qingting Xu; Yu Hong. Springer Science and Business Media Deutschland GmbH, 2023. p. 576-587 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14302 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Enhancing Detailed Feedback to Chinese Writing Learners Using a Soft-Label Driven Approach and Tag-Aware Ranking Model

AU - Cai, Yuzhe

AU - Mao, Shaoguang

AU - Wang, Chenshuo

AU - Ge, Tao

AU - Wu, Wenshan

AU - Xia, Yan

AU - Zheng, Chanjin

AU - Guan, Qiang

PY - 2023

Y1 - 2023

N2 - This paper focuses on providing detailed and specific feedback for Chinese writing learners, which is challenging due to the uncertainty of the feedback space. 30 common tags are identified based on clustering 363k comment phrases. By predicting the corresponding tags for an input, learners can gain insights on how to improve their work. However, the various tag types and non-exhaustive annotation pose challenges for model training. To address this, we propose a soft-label-driven approach to construct a more accurate relationship between samples and the tag space. We use a relevance matrix between different tags to adjust the positive label’s confidence across the entire tag space. Additionally, we propose a tag-aware regression-based ranking model that further improves performance. Our experiments demonstrate that the proposed soft-label approach performs better than a hard-label approach, and the proposed ranking model enhances performance. Our data and code are available at: https://github.com/Zhe0311/DetailedFeedback2ChineseWritingLearners.

AB - This paper focuses on providing detailed and specific feedback for Chinese writing learners, which is challenging due to the uncertainty of the feedback space. 30 common tags are identified based on clustering 363k comment phrases. By predicting the corresponding tags for an input, learners can gain insights on how to improve their work. However, the various tag types and non-exhaustive annotation pose challenges for model training. To address this, we propose a soft-label-driven approach to construct a more accurate relationship between samples and the tag space. We use a relevance matrix between different tags to adjust the positive label’s confidence across the entire tag space. Additionally, we propose a tag-aware regression-based ranking model that further improves performance. Our experiments demonstrate that the proposed soft-label approach performs better than a hard-label approach, and the proposed ranking model enhances performance. Our data and code are available at: https://github.com/Zhe0311/DetailedFeedback2ChineseWritingLearners.

KW - Automated feedback

KW - Computer-aided language learning

KW - Essay assessment

KW - Soft-label tag-aware ranking model

UR - https://www.scopus.com/pages/publications/85174709174

U2 - 10.1007/978-3-031-44693-1_45

DO - 10.1007/978-3-031-44693-1_45

M3 - 会议稿件

AN - SCOPUS:85174709174

SN - 9783031446924

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 576

EP - 587

BT - Natural Language Processing and Chinese Computing - 12th National CCF Conference, NLPCC 2023, Proceedings

A2 - Liu, Fei

A2 - Duan, Nan

A2 - Xu, Qingting

A2 - Hong, Yu

PB - Springer Science and Business Media Deutschland GmbH

T2 - 12th National CCF Conference on Natural Language Processing and Chinese Computing, NLPCC 2023

Y2 - 12 October 2023 through 15 October 2023

ER -

Cai Y, Mao S, Wang C, Ge T, Wu W, Xia Y et al. Enhancing Detailed Feedback to Chinese Writing Learners Using a Soft-Label Driven Approach and Tag-Aware Ranking Model. In Liu F, Duan N, Xu Q, Hong Y, editors, Natural Language Processing and Chinese Computing - 12th National CCF Conference, NLPCC 2023, Proceedings. Springer Science and Business Media Deutschland GmbH. 2023. p. 576-587. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-44693-1_45

Enhancing Detailed Feedback to Chinese Writing Learners Using a Soft-Label Driven Approach and Tag-Aware Ranking Model

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