Yi-Ting Tsai


2021

pdf
Improved Text Classification of Long-term Care Materials
Yi Fan Chiang | Chi-Ling Lee | Heng-Chia Liao | Yi-Ting Tsai | Yu-Yun Chang
Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing (ROCLING 2021)

Aging populations have posed a challenge to many countries including Taiwan, and with them come the issue of long-term care. Given the current context, the aim of this study was to explore the hotly-discussed subtopics in the field of long-term care, and identify its features through NLP. This study applied TF-IDF, the Logistic Regression model, and the Naive Bayes classifier to process data. In sum, the results showed that it reached a best F1-score of 0.920 in identification, and a best accuracy of 0.708 in classification. The results of this study could be used as a reference for future long-term care related applications.

2019

pdf
Adversarial Attack on Sentiment Classification
Yi-Ting Tsai | Min-Chu Yang | Han-Yu Chen
Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP

In this paper, we propose a white-box attack algorithm called “Global Search” method and compare it with a simple misspelling noise and a more sophisticated and common white-box attack approach called “Greedy Search”. The attack methods are evaluated on the Convolutional Neural Network (CNN) sentiment classifier trained on the IMDB movie review dataset. The attack success rate is used to evaluate the effectiveness of the attack methods and the perplexity of the sentences is used to measure the degree of distortion of the generated adversarial examples. The experiment results show that the proposed “Global Search” method generates more powerful adversarial examples with less distortion or less modification to the source text.