19 citations · 35 across the 6 of their papers we have counts for
6 papers
Systematic Evaluation of Predictive Fairness
Xudong Han, Aili Shen, Trevor Cohn +2
Mitigating bias in training on biased datasets is an important open problem. Several techniques have been proposed, however the typical evaluation regime is very limited, consideri…
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…
Contrastive Learning for Fair Representations
Aili Shen, Xudong Han, Trevor Cohn +2
Trained classification models can unintentionally lead to biased representations and predictions, which can reinforce societal preconceptions and stereotypes. Existing debiasing me…
Evaluating Document Coherence Modelling
Aili Shen, Meladel Mistica, Bahar Salehi +3
While pretrained language models ("LM") have driven impressive gains over morpho-syntactic and semantic tasks, their ability to model discourse and pragmatic phenomena is less clea…
A Joint Model for Multimodal Document Quality Assessment
Aili Shen, Bahar Salehi, Timothy Baldwin +1
The quality of a document is affected by various factors, including grammaticality, readability, stylistics, and expertise depth, making the task of document quality assessment a c…