Abstract
In the field of education, understanding students' opinions through their comments is crucial, especially in the Vietnamese language, where resources remain limited. Existing educational datasets often lack domain relevance and student slang. To address these gaps, we introduce NEU-ESC, a new Vietnamese dataset for Educational Sentiment Classification and Topic Classification, curated from university forums, which offers more samples, richer class diversity, longer texts, and broader vocabulary. In addition, we explore multitask learning using encoder-only language models (BERT), in which we showed that it achieves performance up to 83.7% and 79.8% accuracy for sentiment and topic classification tasks. We also benchmark our dataset and model with other datasets and models, including Large Language Models, and discuss these benchmarks. The dataset is publicly available at: this https URL.
Abstract (translated)
在教育领域，通过学生的评论了解他们的意见至关重要，特别是在越南语中，由于资源有限，这一挑战更加突出。现有的教育数据集通常缺乏领域的相关性和学生用语的多样性。为了弥补这些不足，我们推出了NEU-ESC，这是一个新的越南语数据集，用于教育情感分类和话题分类，该数据集是从大学论坛上收集整理而来，提供了更多的样本、更丰富的类别多样性、更长的文章长度以及更为广泛的词汇量。此外，我们还探索了使用编码器单独的语言模型（如BERT）进行多任务学习，在这种情况下，我们展示了它在情感分类和话题分类任务中分别达到了高达83.7%和79.8%的准确性。我们还用其他数据集和模型包括大型语言模型对我们的数据集和模型进行了基准测试，并讨论了这些基准的结果。该数据集可在此URL公开获取：this https URL。
URL
https://arxiv.org/abs/2506.23524
