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

• February 2022: Appointed interim Director of the ITTC on medtech, taking over from Tim Baldwin
• November 2020: EMNLP 2020 will be run online
• July 2020: My student 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

• Chunhua Liu, Trevor Cohn, Simon Deyne and Lea Frermann (2022). WAX: A New Dataset for Word Association eXplanations. In AACL-IJCNLP 2022.
• Thinh Truong, Yulia Otmakhova, Timothy Baldwin, Trevor Cohn, Jey Lau and Karin Verspoor (2022). Not another Negation Benchmark: The NaN-NLI Test Suite for Sub-clausal Negation. In AACL-IJCNLP 2022.
• Jun Wang, Xuanli He, Benjamin Rubinstein and Trevor Cohn (2022). Foiling Training-Time Attacks on Neural Machine Translation Systems. In EMNLP-Findings 2022.
• Aili Shen, Xudong Han, Trevor Cohn, Timothy Baldwin and Lea Frermann (2022). Does Representational Fairness Imply Empirical Fairness?. In AACL-IJCNLP 2022.
• Xudong Han, Aili Shen, Yitong Li, Lea Frermann, Timothy Baldwin and Trevor Cohn (2022). FairLib: A Unified Framework for Assessing and Improving Fairness. In EMNLP-Demos 2022.
• Xudong Han, Aili Shen, Trevor Cohn, Timothy Baldwin and Lea Frermann (2022). Systematic Evaluation of Predictive Fairness. In AACL-IJCNLP 2022.
• Xudong Han, Timothy Baldwin and Trevor Cohn (2022). Balancing out Bias: Achieving Fairness Through Balanced Training. In EMNLP 2022.
• Thinh Truong, Timothy Baldwin, Trevor Cohn and Karin Verspoor (2022). Improving negation detection with negation-focused pre-training. In NAACL 2022.
• Aili Shen, Xudong Han, Trevor Cohn, Timothy Baldwin and Lea Frermann (2022). Optimising Equal Opportunity Fairness in Model Training. In NAACL 2022.
• Kemal Kurniawan, Lea Frermann, Philip Schulz and Trevor Cohn (2022). Unsupervised Cross-Lingual Transfer of Structured Predictors without Source Data. In NAACL 2022.
• Yuan Li, Biaoyan Fang, Jiayuan He, Hiyori Yoshikawa, Saber Akhondi, Christian Druckenbrodt, Camilo Thorne, Zenan Zhai, Zubair Afzal, Trevor Cohn, Timothy Baldwin and Karin Verspoor (2022). The ChEMU 2022 Evaluation Campaign: Information Extraction in Chemical Patents. In ECIR 2022.
• Jun Wang, Benjamin Rubinstein and Trevor Cohn (2022). Measuring and Mitigating Name Biases in Neural Machine Translation. In ACL 2022.
• Fan Jiang and Trevor Cohn (2022). Incorporating Constituent Syntax for Coreference Resolution. In AAAI 2022.

• Zenan Zhai, Christian Druckenbrodt, Camilo Thorne, Saber Akhondi, Dat Nguyen, Trevor Cohn and Karin Verspoor (2021). ChemTables: a dataset for semantic classification on tables in chemical patents. In J. Cheminformatics, Vol 13.
• Jiayuan He, Dat Nguyen, Saber Akhondi, Christian Druckenbrodt, Camilo Thorne, Ralph Hoessel, Zubair Afzal, Zenan Zhai, Biaoyan Fang, Hiyori Yoshikawa, Ameer Albahem, Lawrence Cavedon, Trevor Cohn, Timothy Baldwin and Karin Verspoor (2021). ChEMU 2020: Natural Language Processing Methods Are Effective for Information Extraction From Chemical Patents. In Frontiers Res. Metrics Anal., Vol 6.
• Chang Xu, Jun Wang, Yuqing Tang, Francisco Guzm'an, Benjamin Rubinstein and Trevor Cohn (2021). A Targeted Attack on Black-Box Neural Machine Translation with Parallel Data Poisoning. In WWW 2021.
• Kemal Kurniawan, Lea Frermann, Philip Schulz and Trevor Cohn (2021). PTST-UoM at SemEval-2021 Task 10: Parsimonious Transfer for Sequence Tagging. In SEMEVAL 2021.
• Shima Khanehzar, Trevor Cohn, Gosia Mikolajczak, Andrew Turpin and Lea Frermann (2021). Framing Unpacked: A Semi-Supervised Interpretable Multi-View Model of Media Frames. In NAACL-HLT 2021.
• Fan Jiang and Trevor Cohn (2021). Incorporating Syntax and Semantics in Coreference Resolution with Heterogeneous Graph Attention Network. In NAACL-HLT 2021.
• Jiuzhou Han, Daniel Beck and Trevor Cohn (2021). Generating Diverse Descriptions from Semantic Graphs. In INLG 2021.
• Jinming Zhao, Philip Arthur, Gholamreza Haffari, Trevor Cohn and Ehsan Shareghi (2021). It Is Not As Good As You Think! Evaluating Simultaneous Machine Translation on Interpretation Data. In EMNLP 2021.
• Chang Xu, Jun Wang, Francisco Guzm'an, Benjamin Rubinstein and Trevor Cohn (2021). Mitigating Data Poisoning in Text Classification with Differential Privacy. In Findings of EMNLP 2021.
• Shivashankar Subramanian, Xudong Han, Timothy Baldwin, Trevor Cohn and Lea Frermann (2021). Evaluating Debiasing Techniques for Intersectional Biases. In EMNLP 2021.
• Shivashankar Subramanian, Afshin Rahimi, Timothy Baldwin, Trevor Cohn and Lea Frermann (2021). Fairness-aware Class Imbalanced Learning. In EMNLP 2021.
• Jiayuan He, Biaoyan Fang, Hiyori Yoshikawa, Yuan Li, Saber Akhondi, Christian Druckenbrodt, Camilo Thorne, Zubair Afzal, Zenan Zhai, Lawrence Cavedon, Trevor Cohn, Timothy Baldwin and Karin Verspoor (2021). ChEMU 2021: Reaction Reference Resolution and Anaphora Resolution in Chemical Patents. In ECIR 2021.
• Kemal Kurniawan, Lea Frermann, Philip Schulz and Trevor Cohn (2021). PPT: Parsimonious Parser Transfer for Unsupervised Cross-Lingual Adaptation. In EACL 2021.
• Xudong Han, Timothy Baldwin and Trevor Cohn (2021). Diverse Adversaries for Mitigating Bias in Training. In EACL 2021.
• Philip Arthur, Trevor Cohn and Gholamreza Haffari (2021). Learning Coupled Policies for Simultaneous Machine Translation using Imitation Learning. In EACL 2021.
• Chunhua Liu, Trevor Cohn and Lea Frermann (2021). Commonsense Knowledge in Word Associations and ConceptNet. In CoNLL 2021.
• Jun Wang, Chang Xu, Francisco Guzm'an, Ahmed El-Kishky, Yuqing Tang, Benjamin Rubinstein and Trevor Cohn (2021). Putting words into the system's mouth: A targeted attack on neural machine translation using monolingual data poisoning. In Findings of ACL/IJCNLP 2021.
• Jun Wang, Chang Xu, Francisco Guzm'an, Ahmed El-Kishky, Benjamin Rubinstein and Trevor Cohn (2021). As Easy as 1, 2, 3: Behavioural Testing of NMT Systems for Numerical Translation. In Findings of ACL/IJCNLP 2021.
• Xudong Han, Timothy Baldwin and Trevor Cohn (2021). Decoupling Adversarial Training for Fair NLP. In Findings of ACL/IJCNLP 2021.