Abstract
Automated essay scoring aims to score an essay automatically without any human assistance. Traditional methods heavily rely on manual feature engineering, making it expensive to extract the features. Some recent studies used neural-network-based scoring models to avoid feature engineering. Most of them used CNN or RNN to learn the representation of the essay. Although these models can cope with relationships between words within a short distance, they are limited in capturing long-distance relationships across sentences. In particular, it is difficult to assess the coherence of the essay, which is an essential criterion in essay scoring. In this paper, we use self-attention to capture useful long-distance relationships between words so as to estimate a coherence score. We tested our model on two datasets (ASAP and a new non-native speaker dataset). In both cases, our model outperforms the existing state-of-the-art models.
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Notes
- 1.
The essay is from prompt 6 of ASAP dataset - https://www.kaggle.com/c/asap-aes/data. We only show some of the strong relationships for clarity.
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Acknowledgement
This work is supported by the National Science Foundation of China (61402119) and Special Funds for the Cultivation of Guangdong College Students’ Scientific and Technological Innovation. (“Climbing Program” Special Funds.)
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Li, X., Chen, M., Nie, J., Liu, Z., Feng, Z., Cai, Y. (2018). Coherence-Based Automated Essay Scoring Using Self-attention. In: Sun, M., Liu, T., Wang, X., Liu, Z., Liu, Y. (eds) Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data. CCL NLP-NABD 2018 2018. Lecture Notes in Computer Science(), vol 11221. Springer, Cham. https://doi.org/10.1007/978-3-030-01716-3_32
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