@inproceedings{lu-chen-2021-ntust,
    title = "ntust-nlp-2 at {ROCLING}-2021 Shared Task: {BERT}-based semantic analyzer with word-level information",
    author = "Lu, Ke-Han  and
      Chen, Kuan-Yu",
    editor = "Lee, Lung-Hao  and
      Chang, Chia-Hui  and
      Chen, Kuan-Yu",
    booktitle = "Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing (ROCLING 2021)",
    month = oct,
    year = "2021",
    address = "Taoyuan, Taiwan",
    publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
    url = "https://preview.aclanthology.org/fix-sig-urls/2021.rocling-1.47/",
    pages = "360--366",
    abstract = "In this paper, we proposed a BERT-based dimensional semantic analyzer, which is designed by incorporating with word-level information. Our model achieved three of the best results in four metrics on ``ROCLING 2021 Shared Task: Dimensional Sentiment Analysis for Educational Texts''. We conducted a series of experiments to compare the effectiveness of different pre-trained methods. Besides, the results also proofed that our method can significantly improve the performances than classic methods. Based on the experiments, we also discussed the impact of model architectures and datasets."
}