@inproceedings{182d059b50374976adf9e019a36a0b4d,
title = "Improving Generation of Sentiment Commonsense by Bias Mitigation",
abstract = "Commonsense knowledge graphs (CSKG) are crucial for artificial intelligence systems to understand natural language. Recently, with the construction of COMET (Commonsense Transformer) and ATOMIC2020, a comprehensive coverage commonsense reasoning knowledge graph, CSKG research is increasingly vital in natural language understanding and reasoning. Since sentiment commonsense knowledge is understudied yet, our work focuses on improving the generation of sentiment commonsense in ATOMIC2020. We first show a problem in natural language generation that degrades the accuracy due to the unbalanced sentiment distribution in the dataset. Next, we propose the EDA (Easy Data Augmentation) and UDA(Unsupervised Data Augmentation) based methods that improve the accuracy through biased mitigation of the unbalanced dataset. Our experimental results show that EDA method has little effect on the accuracy, while UDA-based method has some accuracy improvements in ROUGE-I, ROUGE-2, and ROUGE-L.",
keywords = "Bias, Commonsense, EDA, Sentiment, UDA",
author = "Lee, {Jin Kyu} and Jihie Kim",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE International Conference on Big Data and Smart Computing, BigComp 2023 ; Conference date: 13-02-2023 Through 16-02-2023",
year = "2023",
doi = "10.1109/BigComp57234.2023.00061",
language = "English",
series = "Proceedings - 2023 IEEE International Conference on Big Data and Smart Computing, BigComp 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "308--311",
editor = "Hyeran Byun and Ooi, {Beng Chin} and Katsumi Tanaka and Sang-Won Lee and Zhixu Li and Akiyo Nadamoto and Giltae Song and Young-guk Ha and Kazutoshi Sumiya and Wu Yuncheng and Hyuk-Yoon Kwon and Takehiro Yamamoto",
booktitle = "Proceedings - 2023 IEEE International Conference on Big Data and Smart Computing, BigComp 2023",
address = "United States",
}