25 citations · 57 across the 9 of their papers we have counts for
12 papers
Casual Conversations v2: Designing a large consent-driven dataset to measure algorithmic bias and robustness
Caner Hazirbas, Yejin Bang, Tiezheng Yu +9
Developing robust and fair AI systems require datasets with comprehensive set of labels that can help ensure the validity and legitimacy of relevant measurements. Recent efforts, t…
Enabling Classifiers to Make Judgements Explicitly Aligned with Human Values
Yejin Bang, Tiezheng Yu, Andrea Madotto +3
Many NLP classification tasks, such as sexism/racism detection or toxicity detection, are based on human values. Yet, human values can vary under diverse cultural conditions. There…
NeuS: Neutral Multi-News Summarization for Mitigating Framing Bias
Nayeon Lee, Yejin Bang, Tiezheng Yu +2
Media news framing bias can increase political polarization and undermine civil society. The need for automatic mitigation methods is therefore growing. We propose a new task, a ne…
Assessing Political Prudence of Open-domain Chatbots
Yejin Bang, Nayeon Lee, Etsuko Ishii +2
Politically sensitive topics are still a challenge for open-domain chatbots. However, dealing with politically sensitive content in a responsible, non-partisan, and safe behavior w…
Weakly-supervised Multi-task Learning for Multimodal Affect Recognition
Wenliang Dai, Samuel Cahyawijaya, Yejin Bang +1
Multimodal affect recognition constitutes an important aspect for enhancing interpersonal relationships in human-computer interaction. However, relevant data is hard to come by and…
Dynamically Addressing Unseen Rumor via Continual Learning
Nayeon Lee, Andrea Madotto, Yejin Bang +1
Rumors are often associated with newly emerging events, thus, an ability to deal with unseen rumors is crucial for a rumor veracity classification model. Previous works address thi…