19 citations · 32 across the 7 of their papers we have counts for
7 papers · 1 filter
Unsupervised Cross-Lingual Transfer of Structured Predictors without Source Data
Kemal Kurniawan, Lea Frermann, Philip Schulz +1
Providing technologies to communities or domains where training data is scarce or protected e.g., for privacy reasons, is becoming increasingly important. To that end, we generalis…
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…
Fairness-aware Class Imbalanced Learning
Shivashankar Subramanian, Afshin Rahimi, Timothy Baldwin +2
Class imbalance is a common challenge in many NLP tasks, and has clear connections to bias, in that bias in training data often leads to higher accuracy for majority groups at the…
Evaluating Debiasing Techniques for Intersectional Biases
Shivashankar Subramanian, Xudong Han, Timothy Baldwin +2
Bias is pervasive in NLP models, motivating the development of automatic debiasing techniques. Evaluation of NLP debiasing methods has largely been limited to binary attributes in…
Commonsense Knowledge in Word Associations and ConceptNet
Chunhua Liu, Trevor Cohn, Lea Frermann
Humans use countless basic, shared facts about the world to efficiently navigate in their environment. This commonsense knowledge is rarely communicated explicitly, however, unders…
Framing Unpacked: A Semi-Supervised Interpretable Multi-View Model of Media Frames
Shima Khanehzar, Trevor Cohn, Gosia Mikolajczak +2
Understanding how news media frame political issues is important due to its impact on public attitudes, yet hard to automate. Computational approaches have largely focused on class…