每年專案
摘要
A huge number of scientific papers have been authored by non-native English speakers. There is a large demand for effective computer-based writing tools to help writers composing scientific articles. The Automated Evaluation of Scientific Writing (AESW) shared task seeks to promote the use of NLP tools for improving the quality of scientific writing in English by predicting whether a given sentence needs language editing or not. In this study, we propose an ensemble multi-channel BiLSTM-CNN model based on a series of experiments in comparing the number of channels, network architectures, and ensemble size. Our model achieved an F1 score of 63.28 outperforms participating systems in the AESW 2016 task.
貢獻的翻譯標題 | Scientific Writing Evaluation Using Ensemble Multi-channel Neural Networks |
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原文 | 繁體中文 |
主出版物標題 | ROCLING 2020 - 32nd Conference on Computational Linguistics and Speech Processing |
編輯 | Jenq-Haur Wang, Ying-Hui Lai, Lung-Hao Lee, Kuan-Yu Chen, Hung-Yi Lee, Chi-Chun Lee, Syu-Siang Wang, Hen-Hsen Huang, Chuan-Ming Liu |
發行者 | The Association for Computational Linguistics and Chinese Language Processing (ACLCLP) |
頁面 | 359-371 |
頁數 | 13 |
ISBN(電子) | 9789869576932 |
出版狀態 | 已出版 - 2020 |
事件 | 32nd Conference on Computational Linguistics and Speech Processing, ROCLING 2020 - Taipei, Taiwan 持續時間: 24 9月 2020 → 26 9月 2020 |
出版系列
名字 | ROCLING 2020 - 32nd Conference on Computational Linguistics and Speech Processing |
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???event.eventtypes.event.conference??? | 32nd Conference on Computational Linguistics and Speech Processing, ROCLING 2020 |
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國家/地區 | Taiwan |
城市 | Taipei |
期間 | 24/09/20 → 26/09/20 |
Keywords
- Automated Writing Evaluation
- Ensemble Learning
- Multi-channel Neural Networks
- Scientific English
指紋
深入研究「運用集成式多通道類神經網路於科技英文寫作評估」主題。共同形成了獨特的指紋。專案
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