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· Trevor Cohn
Trevor Cohn
Professor Level 4, Melbourne Connect
Professor Level 4, Melbourne Connect
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. My core focus is on probabilistic machine learning modelling of language applications, particularly handling uncertain or partly observed data and structured prediction problems.
News
- April 2023-: I now work for Google Research Australia in Sydney. This is a dual appointment, however the majority of my time is at Google, and I will not be taking on new students nor teaching etc.
- 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
- Sukai Huang, Nir Lipovetzky and Trevor Cohn (2025). Planning in the Dark: LLM-Symbolic Planning Pipeline without Experts. In AAAI 2025.
- Sukai Huang, Nir Lipovetzky and Trevor Cohn (2025). Chasing Progress, Not Perfection: Revisiting Strategies for End-to-End LLM Plan Generation. In LM4Plan@AAAI 2025.
- Xuanli He, Qiongkai Xu, Jun Wang, Benjamin Rubinstein and Trevor Cohn (2024). SEEP: Training Dynamics Grounds Latent Representation Search for Mitigating Backdoor Poisoning Attacks. In Trans. Assoc. Comput. Linguistics, Vol 12.
- Zheng Lim, Harry Stuart, Simon Deyne, Terry Regier, Ekaterina Vylomova, Trevor Cohn and Charles Kemp (2024). A Computational Approach to Identifying Cultural Keywords Across Languages. In Cogn. Sci., Vol 48.
- Jun Wang, Qiongkai Xu, Xuanli He, Benjamin Rubinstein and Trevor Cohn (2024). Backdoor Attacks on Multilingual Machine Translation. In NAACL 2024.
- Thinh Truong, Yulia Otmakhova, Karin Verspoor, Trevor Cohn and Timothy Baldwin (2024). Revisiting subword tokenization: A case study on affixal negation in large language models. In NAACL 2024.
- Fan Jiang, Tom Drummond and Trevor Cohn (2024). Pre-training Cross-lingual Open Domain Question Answering with Large-scale Synthetic Supervision. In EMNLP 2024.
- Zheng Lim, Ekaterina Vylomova, Trevor Cohn and Charles Kemp (2024). Simpson's Paradox and the Accuracy-Fluency Tradeoff in Translation. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics.