69 citations · 203 across the 11 of their papers we have counts for
8 papers · 1 filter
On Positivity Bias in Negative Reviews
Madhusudhan Aithal, Chenhao Tan
Prior work has revealed that positive words occur more frequently than negative words in human expressions, which is typically attributed to positivity bias, a tendency for people…
Evaluating and Characterizing Human Rationales
Samuel Carton, Anirudh Rathore, Chenhao Tan
Two main approaches for evaluating the quality of machine-generated rationales are: 1) using human rationales as a gold standard; and 2) automated metrics based on how rationales a…
Characterizing the Value of Information in Medical Notes
Chao-Chun Hsu, Shantanu Karnwal, Sendhil Mullainathan +2
Machine learning models depend on the quality of input data. As electronic health records are widely adopted, the amount of data in health care is growing, along with complaints ab…
What Gets Echoed? Understanding the "Pointers" in Explanations of Persuasive Arguments
David Atkinson, Kumar Bhargav Srinivasan, Chenhao Tan
Explanations are central to everyday life, and are a topic of growing interest in the AI community. To investigate the process of providing natural language explanations, we levera…
Many Faces of Feature Importance: Comparing Built-in and Post-hoc Feature Importance in Text Classification
Vivian Lai, Jon Z. Cai, Chenhao Tan
Feature importance is commonly used to explain machine predictions. While feature importance can be derived from a machine learning model with a variety of methods, the consistency…
No Permanent Friends or Enemies: Tracking Relationships between Nations from News
Xiaochuang Han, Eunsol Choi, Chenhao Tan
Understanding the dynamics of international politics is important yet challenging for civilians. In this work, we explore unsupervised neural models to infer relations between nati…