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.


  • We hosted ACL 2018 in Melbourne, with Tim Baldwin, Karin Verspoor and myself serving as the local chairs.
  • My group has continued publishing success, with papers at ICML, WWW, NAACL, ICASSP and ACL (x3) in 2019

Current Projects

  • Efficient storage and access to text count data: An application to unlimited order language modelling. 2016 – 2017. Google Research Award, $US 85k.
  • Learning Deep Semantics for Automatic Translation between Human Languages. 2016 – 2019. ARC Discovery with Reza Haffari, $450k.
  • Ariel: Analysis of Rare Incident-Event Languages. 2015 – 2018. DARPA LORELEI (sub-contract), $300k.
  • Adaptive Context-Dependent Machine Translation for Heterogeneous Text. 2014 – 2018. ARC Future Fellowship, $730k.
  • Pheme: Computing Veracity Across Media, Languages, and Social Networks. 2014 – 2017. EU FP7 with Kalina Bontcheva and others, £494k.

Selected Papers

Exploiting Worker Correlation for Label Aggregation in Crowdsourcing
Yuan Li, Benjamin I. P. Rubinstein and Trevor Cohn. In Proceedings of ICML, 2019.
Multilingual NER Transfer for Low-resource Languages
Afshin Rahimi, Yuan Li and Trevor Cohn. In Proceedings of ACL, 2019.
Semi-supervised Stochastic Domain Adaptation using Variational Inference
Yitong Li, Timothy Baldwin and Trevor Cohn. In Proceedings of ACL, 2019.
Putting Evaluation in Context: Contextual Embeddings improve Machine Translation Evaluation
Nitika Mathur, Timothy Baldwin and Trevor Cohn. In Proceedings of ACL (short), 2019.
Contextualization of Morphological Inflection
Ekaterina Vylomova, Ryan Cotterell, Trevor Cohn, Timothy Baldwin and Jason Eisner. In Proceedings of NAACL (short), 2019.
Truth inference at scale: A Bayesian model for adjudicating highly redundant crowd annotations
Yuan Li, Benjamin I. P. Rubinstein and Trevor Cohn. In Proceedings of WWW, 2019.
A unified neural architecture for instrumental audio tasks
Steven Spratley, Daniel Beck and Trevor Cohn. In Proceedings of ICASSP, 2019.