@inproceedings{wang-etal-2021-ntust,
title = "ntust-nlp-1 at {ROCLING}-2021 Shared Task: Educational Texts Dimensional Sentiment Analysis using Pretrained Language Models",
author = "Wang, Yi-Wei and
Chang, Wei-Zhe and
Fang, Bo-Han and
Chen, Yi-Chia and
Huang, Wei-Kai 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://aclanthology.org/2021.rocling-1.46",
pages = "354--359",
abstract = "This technical report aims at the ROCLING 2021 Shared Task: Dimensional Sentiment Analysis for Educational Texts. In order to predict the affective states of Chinese educational texts, we present a practical framework by employing pre-trained language models, such as BERT and MacBERT. Several valuable observations and analyses can be drawn from a series of experiments. From the results, we find that MacBERT-based methods can deliver better results than BERT-based methods on the verification set. Therefore, we average the prediction results of several models obtained using different settings as the final output.",
}
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%0 Conference Proceedings
%T ntust-nlp-1 at ROCLING-2021 Shared Task: Educational Texts Dimensional Sentiment Analysis using Pretrained Language Models
%A Wang, Yi-Wei
%A Chang, Wei-Zhe
%A Fang, Bo-Han
%A Chen, Yi-Chia
%A Huang, Wei-Kai
%A Chen, Kuan-Yu
%Y Lee, Lung-Hao
%Y Chang, Chia-Hui
%Y Chen, Kuan-Yu
%S Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing (ROCLING 2021)
%D 2021
%8 October
%I The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)
%C Taoyuan, Taiwan
%F wang-etal-2021-ntust
%X This technical report aims at the ROCLING 2021 Shared Task: Dimensional Sentiment Analysis for Educational Texts. In order to predict the affective states of Chinese educational texts, we present a practical framework by employing pre-trained language models, such as BERT and MacBERT. Several valuable observations and analyses can be drawn from a series of experiments. From the results, we find that MacBERT-based methods can deliver better results than BERT-based methods on the verification set. Therefore, we average the prediction results of several models obtained using different settings as the final output.
%U https://aclanthology.org/2021.rocling-1.46
%P 354-359
Markdown (Informal)
[ntust-nlp-1 at ROCLING-2021 Shared Task: Educational Texts Dimensional Sentiment Analysis using Pretrained Language Models](https://aclanthology.org/2021.rocling-1.46) (Wang et al., ROCLING 2021)
ACL