Preprints

  1. Soto, R. R., Koch, K., Khan, A., Chen, B., Bishop, M., & Andrews, N. (2024). Few-Shot Detection of Machine-Generated Text using Style Representations.
  2. Khan, A., Wang, A., Hager, S., & Andrews, N. (2023). Learning to Generate Text in Arbitrary Writing Styles.
  3. Weller, O., Khan, A., Weir, N., Lawrie, D., & Durme, B. V. (2023). Defending Against Misinformation Attacks in Open-Domain Question Answering.

Refereed conference proceedings

  1. Rivera-Soto, R. A., Miano, O. E., Ordonez, J., Chen, B. Y., Khan, A., Bishop, M., & Andrews, N. (2021). Learning Universal Authorship Representations. In M.-F. Moens, X. Huang, L. Specia, & S. W.-tau Yih (Eds.), Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (pp. 913–919). Association for Computational Linguistics. https://aclanthology.org/2021.emnlp-main.70
  2. Khan, A., Fleming, E., Schofield, N., Bishop, M., & Andrews, N. (2021). A Deep Metric Learning Approach to Account Linking. In K. Toutanova, A. Rumshisky, L. Zettlemoyer, D. Hakkani-Tur, I. Beltagy, S. Bethard, R. Cotterell, T. Chakraborty, & Y. Zhou (Eds.), Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (pp. 5275–5287). Association for Computational Linguistics. https://aclanthology.org/2021.naacl-main.415