Yu Hou


2022

pdf bib
On Measures of Biases and Harms in NLP
Sunipa Dev | Emily Sheng | Jieyu Zhao | Aubrie Amstutz | Jiao Sun | Yu Hou | Mattie Sanseverino | Jiin Kim | Akihiro Nishi | Nanyun Peng | Kai-Wei Chang
Findings of the Association for Computational Linguistics: AACL-IJCNLP 2022

Recent studies show that Natural Language Processing (NLP) technologies propagate societal biases about demographic groups associated with attributes such as gender, race, and nationality. To create interventions and mitigate these biases and associated harms, it is vital to be able to detect and measure such biases. While existing works propose bias evaluation and mitigation methods for various tasks, there remains a need to cohesively understand the biases and the specific harms they measure, and how different measures compare with each other. To address this gap, this work presents a practical framework of harms and a series of questions that practitioners can answer to guide the development of bias measures. As a validation of our framework and documentation questions, we also present several case studies of how existing bias measures in NLP—both intrinsic measures of bias in representations and extrinsic measures of bias of downstream applications—can be aligned with different harms and how our proposed documentation questions facilitates more holistic understanding of what bias measures are measuring.

2021

pdf bib
COM2SENSE: A Commonsense Reasoning Benchmark with Complementary Sentences
Shikhar Singh | Nuan Wen | Yu Hou | Pegah Alipoormolabashi | Te-lin Wu | Xuezhe Ma | Nanyun Peng
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021