343 citations · 355 across the 9 of their papers we have counts for
10 papers
Improving negation detection with negation-focused pre-training
Thinh Hung Truong, Timothy Baldwin, Trevor Cohn +1
Negation is a common linguistic feature that is crucial in many language understanding tasks, yet it remains a hard problem due to diversity in its expression in different types of…
Optimising Equal Opportunity Fairness in Model Training
Aili Shen, Xudong Han, Trevor Cohn +2
Real-world datasets often encode stereotypes and societal biases. Such biases can be implicitly captured by trained models, leading to biased predictions and exacerbating existing…
fairlib: A Unified Framework for Assessing and Improving Classification Fairness
Xudong Han, Aili Shen, Yitong Li +3
This paper presents fairlib, an open-source framework for assessing and improving classification fairness. It provides a systematic framework for quickly reproducing existing basel…
Towards Equal Opportunity Fairness through Adversarial Learning
Xudong Han, Timothy Baldwin, Trevor Cohn
Adversarial training is a common approach for bias mitigation in natural language processing. Although most work on debiasing is motivated by equal opportunity, it is not explicitl…
Incorporating Constituent Syntax for Coreference Resolution
Fan Jiang, Trevor Cohn
Syntax has been shown to benefit Coreference Resolution from incorporating long-range dependencies and structured information captured by syntax trees, either in traditional statis…
ITTC @ TREC 2021 Clinical Trials Track
Thinh Hung Truong, Yulia Otmakhova, Rahmad Mahendra +6
This paper describes the submissions of the Natural Language Processing (NLP) team from the Australian Research Council Industrial Transformation Training Centre (ITTC) for Cogniti…