@inproceedings{he-li-2024-zero-shot,
title = "Zero-shot Cross-lingual Automated Essay Scoring",
author = "He, Junyi and
Li, Xia",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.1550",
pages = "17819--17832",
abstract = "Due to the difficulty of creating high-quality labelled training data for different languages, the low-resource problem is crucial yet challenging for automated essay scoring (AES). However, little attention has been paid to addressing this challenge. In this paper, we propose a novel zero-shot cross-lingual scoring method from the perspectives of pretrained multilingual representation and writing quality alignment to score essays in unseen languages. Specifically, we adopt multilingual pretrained language models as the encoder backbone to deeply and comprehensively represent multilingual essays. Motivated by the fact that the scoring knowledge for evaluating writing quality is comparable across different languages, we introduce an innovative strategy for aligning essays in a language-independent manner. The proposed strategy aims to capture shared knowledge from diverse languages, thereby enhancing the representation of essays written in unseen languages with respect to their quality. We include essay datasets in six languages (Czech, German, English, Spanish, Italian and Portuguese) to establish extensive experiments, and the results demonstrate that our method achieves state-of-the-art cross-lingual scoring performance.",
}
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<abstract>Due to the difficulty of creating high-quality labelled training data for different languages, the low-resource problem is crucial yet challenging for automated essay scoring (AES). However, little attention has been paid to addressing this challenge. In this paper, we propose a novel zero-shot cross-lingual scoring method from the perspectives of pretrained multilingual representation and writing quality alignment to score essays in unseen languages. Specifically, we adopt multilingual pretrained language models as the encoder backbone to deeply and comprehensively represent multilingual essays. Motivated by the fact that the scoring knowledge for evaluating writing quality is comparable across different languages, we introduce an innovative strategy for aligning essays in a language-independent manner. The proposed strategy aims to capture shared knowledge from diverse languages, thereby enhancing the representation of essays written in unseen languages with respect to their quality. We include essay datasets in six languages (Czech, German, English, Spanish, Italian and Portuguese) to establish extensive experiments, and the results demonstrate that our method achieves state-of-the-art cross-lingual scoring performance.</abstract>
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%0 Conference Proceedings
%T Zero-shot Cross-lingual Automated Essay Scoring
%A He, Junyi
%A Li, Xia
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F he-li-2024-zero-shot
%X Due to the difficulty of creating high-quality labelled training data for different languages, the low-resource problem is crucial yet challenging for automated essay scoring (AES). However, little attention has been paid to addressing this challenge. In this paper, we propose a novel zero-shot cross-lingual scoring method from the perspectives of pretrained multilingual representation and writing quality alignment to score essays in unseen languages. Specifically, we adopt multilingual pretrained language models as the encoder backbone to deeply and comprehensively represent multilingual essays. Motivated by the fact that the scoring knowledge for evaluating writing quality is comparable across different languages, we introduce an innovative strategy for aligning essays in a language-independent manner. The proposed strategy aims to capture shared knowledge from diverse languages, thereby enhancing the representation of essays written in unseen languages with respect to their quality. We include essay datasets in six languages (Czech, German, English, Spanish, Italian and Portuguese) to establish extensive experiments, and the results demonstrate that our method achieves state-of-the-art cross-lingual scoring performance.
%U https://aclanthology.org/2024.lrec-main.1550
%P 17819-17832
Markdown (Informal)
[Zero-shot Cross-lingual Automated Essay Scoring](https://aclanthology.org/2024.lrec-main.1550) (He & Li, LREC-COLING 2024)
ACL
- Junyi He and Xia Li. 2024. Zero-shot Cross-lingual Automated Essay Scoring. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 17819–17832, Torino, Italia. ELRA and ICCL.