Yixin Wan


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Using Item Response Theory to Measure Gender and Racial Bias of a BERT-based Automated English Speech Assessment System
Alexander Kwako | Yixin Wan | Jieyu Zhao | Kai-Wei Chang | Li Cai | Mark Hansen
Proceedings of the 17th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2022)

Recent advances in natural language processing and transformer-based models have made it easier to implement accurate, automated English speech assessments. Yet, without careful examination, applications of these models may exacerbate social prejudices based on gender and race. This study addresses the need to examine potential biases of transformer-based models in the context of automated English speech assessment. For this purpose, we developed a BERT-based automated speech assessment system and investigated gender and racial bias of examinees’ automated scores. Gender and racial bias was measured by examining differential item functioning (DIF) using an item response theory framework. Preliminary results, which focused on a single verbal-response item, showed no statistically significant DIF based on gender or race for automated scores.

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Improving the Adversarial Robustness of NLP Models by Information Bottleneck
Cenyuan Zhang | Xiang Zhou | Yixin Wan | Xiaoqing Zheng | Kai-Wei Chang | Cho-Jui Hsieh
Findings of the Association for Computational Linguistics: ACL 2022

Existing studies have demonstrated that adversarial examples can be directly attributed to the presence of non-robust features, which are highly predictive, but can be easily manipulated by adversaries to fool NLP models. In this study, we explore the feasibility of capturing task-specific robust features, while eliminating the non-robust ones by using the information bottleneck theory. Through extensive experiments, we show that the models trained with our information bottleneck-based method are able to achieve a significant improvement in robust accuracy, exceeding performances of all the previously reported defense methods while suffering almost no performance drop in clean accuracy on SST-2, AGNEWS and IMDB datasets.