Zhao, Z., N. Aletras (2024). Incorporating Attribution Importance for Improving Faithfulness Metrics. 2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics. NAACL2024, paper
Zhao, Z., N. Aletras (2023). Incorporating Attribution Importance for Improving Faithfulness Metrics. The 61st Annual Meeting of the Association for Computational Linguistics. ACL2023, paper
Zhao, Z., G. Chrysostomou, K. Bontcheva and N. Aletras (2022). On the Impact of Temporal Concept Drift on Model Explanations. In Findings of the Association for Computational Linguistics: EMNLP 2022, paper
Clowes, M., Stansfield, C., Thomas, J., Shemilt, I., Paisley, S., Stevenson, M., Zhao, Z., Marshall, I., Kell, G., (June 2022). All is FAIR in health inequalities research: using machine learning to build a new database of health equity studies. European Association for Health Information and Libraries 2022. project
Zhao, Z., Zhang, Z., Hopfgartner, F. (2022). Utilizing Subjectivity Level to Mitigate Identity Term Bias in Toxic Comments Classification. Online Social Networks and Media, 29, 100205. paper
Zhao, Z., Zhang, Z., Hopfgartner, F. (2021). A Comparative Study of Using Pre-trained Language Models for Toxic Comment Classification. In Companion Proceedings of the Web Conference 2021 (pp. 500-507) WWW 2021, paper
Zhao, Z., Zhang, Z., Hopfgartner, F. (2019). Detecting Toxic Content Online and the Effect of Training Data on Classification Performance. In Proceedings of 20th International Conference on Computational Linguistics and Intelligent Text Processing, La Rochelle, France paper