Trevor Cohn
Professor

Room 323
Doug McDonnell Building (#167)

Computing and Information Systems
The University of Melbourne
Natural Language Processing group

Ph: +61383441732

Understanding of human language by computers has been a central goal of Artificial Intelligence since its beginnings, with massive potential to improve communication, provide better information access and automate basic human tasks. My research focuses on technologies for automatic processing of human language, with several applications including automatic translation (akin to Google and Bing's translation tools). My core focus is on probabilistic machine learning modelling of language applications, particularly handling uncertain or partly observed data and structured prediction problems.

News

• October 2022: Thinh Truong awarded AACL award for best paper runner-up
• July 2022: Nitika Mathur awarded CORE dissertation award
• February 2022: Appointed Director of the ITTC on medtech, taking over from Tim Baldwin
• July 2020: Nitika Mathur was awarded a best paper at ACL 2020 (honorable mention), for Tangled Up in BLEU - Reevaluating the Evaluation of Automatic Machine Translation Evaluation Metrics
• October 2019: I am travelling to Hong Kong for EMNLP, to present two papers in the main conference and two workshop papers.
• September 2019: I will serve as PC chair for EMNLP 2020, alongside Yang Liu and Yulan He.
• July 2019: The group has papers at ICML, WWW, NAACL, ICASSP and ACL (x3).
• July 2018: We hosted ACL 2018 in Melbourne, with Tim Baldwin, Karin Verspoor and myself serving as the local chairs.

Recent Papers

• Shima Khanehzar, Trevor Cohn, Gosia Mikolajczak and Lea Frermann (2023). Probing Power by Prompting: Harnessing Pre-trained Language Models for Power Connotation Frame. In EACL 2023.
• Fan Jiang, Tom Drummond and Trevor Cohn (2023). Don't Mess with Mister-in-Between: Improved Negative Search for Knowledge Graph Completion. In EACL 2023.
• Xudong Han, Timothy Baldwin and Trevor Cohn (2023). Fair Enough: Standardizing Evaluation and Model Selection for Fairness Research in NLP. In EACL 2023.
• Viktoria Schram, Daniel Beck and Trevor Cohn (2023). Performance Prediction via Bayesian Matrix Factorisation for Natural Language Processing Tasks. In EACL 2023.
• Xudong Han, Timothy Baldwin and Trevor Cohn (2023). Everybody Needs Good Neighbours: An Unsupervised Locality-based Method for Bias Mitigation. In ICLR 2023.