Automatic Chinese reading comprehension grading by LSTM with knowledge adaptation

Yuwei Huang; Xi Yang; Fuzhen Zhuang; Lishan Zhang; Shengquan Yu

doi:10.1007/978-3-319-93034-3_10

Automatic Chinese reading comprehension grading by LSTM with knowledge adaptation

Yuwei Huang
, Xi Yang^*
, Fuzhen Zhuang
, Lishan Zhang
, Shengquan Yu

^*Corresponding author for this work

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

15 Citations (Scopus)

Abstract

Owing to the subjectivity of graders and the complexity of assessment standard, grading is a tough problem in the field of education. This paper presents an algorithm for automatic grading of open-ended Chinese reading comprehension questions. Due to the high complexity of feature engineering and the lack of consideration for word order in frequency based word embedding models, we utilize long-short term memory recurrent neural network to extract semantic feature in student answers automatically. In addition, we also try to impose the knowledge adaptation from web corpus to student answers, and represent the students’ responses to vectors which are fed into the memory network. Along this line, the workload of teacher and the subjectivity in reading comprehension grading can both be reduced obviously. What’s more, the automatic grading methods for Chinese reading comprehension will be more thorough. The experimental results on five Chinese and two English data sets demonstrate the superior performance over compared baselines.

Original language	English
Title of host publication	Advances in Knowledge Discovery and Data Mining - 22nd Pacific-Asia Conference, PAKDD 2018, Proceedings
Editors	Dinh Phung, Geoffrey I. Webb, Bao Ho, Vincent S. Tseng, Mohadeseh Ganji, Lida Rashidi
Publisher	Springer Verlag
Pages	118-129
Number of pages	12
ISBN (Print)	9783319930336
DOIs	https://doi.org/10.1007/978-3-319-93034-3_10
Publication status	Published - 2018
Externally published	Yes
Event	22nd Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2018 - Melbourne, Australia Duration: 3 Jun 2018 → 6 Jun 2018

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10937 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	22nd Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2018
Country/Territory	Australia
City	Melbourne
Period	3/06/18 → 6/06/18

Keywords

Automatic grading
Knowledge adaptation
LSTM
Reading comprehension
Text classification

Access to Document

10.1007/978-3-319-93034-3_10

Cite this

Huang, Y., Yang, X., Zhuang, F., Zhang, L., & Yu, S. (2018). Automatic Chinese reading comprehension grading by LSTM with knowledge adaptation. In D. Phung, G. I. Webb, B. Ho, V. S. Tseng, M. Ganji, & L. Rashidi (Eds.), Advances in Knowledge Discovery and Data Mining - 22nd Pacific-Asia Conference, PAKDD 2018, Proceedings (pp. 118-129). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10937 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-319-93034-3_10

Huang, Yuwei ; Yang, Xi ; Zhuang, Fuzhen et al. / Automatic Chinese reading comprehension grading by LSTM with knowledge adaptation. Advances in Knowledge Discovery and Data Mining - 22nd Pacific-Asia Conference, PAKDD 2018, Proceedings. editor / Dinh Phung ; Geoffrey I. Webb ; Bao Ho ; Vincent S. Tseng ; Mohadeseh Ganji ; Lida Rashidi. Springer Verlag, 2018. pp. 118-129 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{d434a4ed0f7240fc89085695d1f76ef1,

title = "Automatic Chinese reading comprehension grading by LSTM with knowledge adaptation",

abstract = "Owing to the subjectivity of graders and the complexity of assessment standard, grading is a tough problem in the field of education. This paper presents an algorithm for automatic grading of open-ended Chinese reading comprehension questions. Due to the high complexity of feature engineering and the lack of consideration for word order in frequency based word embedding models, we utilize long-short term memory recurrent neural network to extract semantic feature in student answers automatically. In addition, we also try to impose the knowledge adaptation from web corpus to student answers, and represent the students{\textquoteright} responses to vectors which are fed into the memory network. Along this line, the workload of teacher and the subjectivity in reading comprehension grading can both be reduced obviously. What{\textquoteright}s more, the automatic grading methods for Chinese reading comprehension will be more thorough. The experimental results on five Chinese and two English data sets demonstrate the superior performance over compared baselines.",

keywords = "Automatic grading, Knowledge adaptation, LSTM, Reading comprehension, Text classification",

author = "Yuwei Huang and Xi Yang and Fuzhen Zhuang and Lishan Zhang and Shengquan Yu",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG, part of Springer Nature 2018.; 22nd Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2018 ; Conference date: 03-06-2018 Through 06-06-2018",

year = "2018",

doi = "10.1007/978-3-319-93034-3\_10",

language = "English",

isbn = "9783319930336",

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

publisher = "Springer Verlag",

pages = "118--129",

editor = "Dinh Phung and Webb, \{Geoffrey I.\} and Bao Ho and Tseng, \{Vincent S.\} and Mohadeseh Ganji and Lida Rashidi",

booktitle = "Advances in Knowledge Discovery and Data Mining - 22nd Pacific-Asia Conference, PAKDD 2018, Proceedings",

address = "Germany",

}

Huang, Y, Yang, X, Zhuang, F, Zhang, L & Yu, S 2018, Automatic Chinese reading comprehension grading by LSTM with knowledge adaptation. in D Phung, GI Webb, B Ho, VS Tseng, M Ganji & L Rashidi (eds), Advances in Knowledge Discovery and Data Mining - 22nd Pacific-Asia Conference, PAKDD 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10937 LNAI, Springer Verlag, pp. 118-129, 22nd Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2018, Melbourne, Australia, 3/06/18. https://doi.org/10.1007/978-3-319-93034-3_10

Automatic Chinese reading comprehension grading by LSTM with knowledge adaptation. / Huang, Yuwei; Yang, Xi; Zhuang, Fuzhen et al.
Advances in Knowledge Discovery and Data Mining - 22nd Pacific-Asia Conference, PAKDD 2018, Proceedings. ed. / Dinh Phung; Geoffrey I. Webb; Bao Ho; Vincent S. Tseng; Mohadeseh Ganji; Lida Rashidi. Springer Verlag, 2018. p. 118-129 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10937 LNAI).

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

TY - GEN

T1 - Automatic Chinese reading comprehension grading by LSTM with knowledge adaptation

AU - Huang, Yuwei

AU - Yang, Xi

AU - Zhuang, Fuzhen

AU - Zhang, Lishan

AU - Yu, Shengquan

N1 - Publisher Copyright: © Springer International Publishing AG, part of Springer Nature 2018.

PY - 2018

Y1 - 2018

N2 - Owing to the subjectivity of graders and the complexity of assessment standard, grading is a tough problem in the field of education. This paper presents an algorithm for automatic grading of open-ended Chinese reading comprehension questions. Due to the high complexity of feature engineering and the lack of consideration for word order in frequency based word embedding models, we utilize long-short term memory recurrent neural network to extract semantic feature in student answers automatically. In addition, we also try to impose the knowledge adaptation from web corpus to student answers, and represent the students’ responses to vectors which are fed into the memory network. Along this line, the workload of teacher and the subjectivity in reading comprehension grading can both be reduced obviously. What’s more, the automatic grading methods for Chinese reading comprehension will be more thorough. The experimental results on five Chinese and two English data sets demonstrate the superior performance over compared baselines.

AB - Owing to the subjectivity of graders and the complexity of assessment standard, grading is a tough problem in the field of education. This paper presents an algorithm for automatic grading of open-ended Chinese reading comprehension questions. Due to the high complexity of feature engineering and the lack of consideration for word order in frequency based word embedding models, we utilize long-short term memory recurrent neural network to extract semantic feature in student answers automatically. In addition, we also try to impose the knowledge adaptation from web corpus to student answers, and represent the students’ responses to vectors which are fed into the memory network. Along this line, the workload of teacher and the subjectivity in reading comprehension grading can both be reduced obviously. What’s more, the automatic grading methods for Chinese reading comprehension will be more thorough. The experimental results on five Chinese and two English data sets demonstrate the superior performance over compared baselines.

KW - Automatic grading

KW - Knowledge adaptation

KW - LSTM

KW - Reading comprehension

KW - Text classification

UR - https://www.scopus.com/pages/publications/85049376691

U2 - 10.1007/978-3-319-93034-3_10

DO - 10.1007/978-3-319-93034-3_10

M3 - Conference contribution

AN - SCOPUS:85049376691

SN - 9783319930336

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

SP - 118

EP - 129

BT - Advances in Knowledge Discovery and Data Mining - 22nd Pacific-Asia Conference, PAKDD 2018, Proceedings

A2 - Phung, Dinh

A2 - Webb, Geoffrey I.

A2 - Ho, Bao

A2 - Tseng, Vincent S.

A2 - Ganji, Mohadeseh

A2 - Rashidi, Lida

PB - Springer Verlag

T2 - 22nd Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2018

Y2 - 3 June 2018 through 6 June 2018

ER -

Huang Y, Yang X, Zhuang F, Zhang L, Yu S. Automatic Chinese reading comprehension grading by LSTM with knowledge adaptation. In Phung D, Webb GI, Ho B, Tseng VS, Ganji M, Rashidi L, editors, Advances in Knowledge Discovery and Data Mining - 22nd Pacific-Asia Conference, PAKDD 2018, Proceedings. Springer Verlag. 2018. p. 118-129. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-93034-3_10

Automatic Chinese reading comprehension grading by LSTM with knowledge adaptation

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Keywords

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