@inproceedings{550ae61744844dd8afaf9d3567b11f5a,
title = "運用集成式多通道類神經網路於科技英文寫作評估",
abstract = "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.",
keywords = "Automated Writing Evaluation, Ensemble Learning, Multi-channel Neural Networks, Scientific English",
author = "Wang, {Yuh Shyang} and Lee, {Lung Hao} and Lin, {Bo Lin} and Yu, {Liang Chih}",
note = "Publisher Copyright: {\textcopyright} ROCLING 2020.All rights reserved.; 32nd Conference on Computational Linguistics and Speech Processing, ROCLING 2020 ; Conference date: 24-09-2020 Through 26-09-2020",
year = "2020",
language = "???core.languages.zh_TW???",
series = "ROCLING 2020 - 32nd Conference on Computational Linguistics and Speech Processing",
publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
pages = "359--371",
editor = "Jenq-Haur Wang and Ying-Hui Lai and Lung-Hao Lee and Kuan-Yu Chen and Hung-Yi Lee and Chi-Chun Lee and Syu-Siang Wang and Hen-Hsen Huang and Chuan-Ming Liu",
booktitle = "ROCLING 2020 - 32nd Conference on Computational Linguistics and Speech Processing",
}