69 citations · 87 across the 9 of their papers we have counts for
9 papers
Learning to Ignore Adversarial Attacks
Yiming Zhang, Yangqiaoyu Zhou, Samuel Carton +1
Despite the strong performance of current NLP models, they can be brittle against adversarial attacks. To enable effective learning against adversarial inputs, we introduce the use…
Human-AI Collaboration via Conditional Delegation: A Case Study of Content Moderation
Vivian Lai, Samuel Carton, Rajat Bhatnagar +3
Despite impressive performance in many benchmark datasets, AI models can still make mistakes, especially among out-of-distribution examples. It remains an open question how such im…
What to Learn, and How: Toward Effective Learning from Rationales
Samuel Carton, Surya Kanoria, Chenhao Tan
Learning from rationales seeks to augment model prediction accuracy using human-annotated rationales (i.e. subsets of input tokens) that justify their chosen labels, often in the f…
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…
Explainable Prediction of Text Complexity: The Missing Preliminaries for Text Simplification
Cristina Garbacea, Mengtian Guo, Samuel Carton +1
Text simplification reduces the language complexity of professional content for accessibility purposes. End-to-end neural network models have been widely adopted to directly genera…
Harnessing Explanations to Bridge AI and Humans
Vivian Lai, Samuel Carton, Chenhao Tan
Machine learning models are increasingly integrated into societally critical applications such as recidivism prediction and medical diagnosis, thanks to their superior predictive p…
Judge the Judges: A Large-Scale Evaluation Study of Neural Language Models for Online Review Generation
Cristina Garbacea, Samuel Carton, Shiyan Yan +1
We conduct a large-scale, systematic study to evaluate the existing evaluation methods for natural language generation in the context of generating online product reviews. We compa…
Extractive Adversarial Networks: High-Recall Explanations for Identifying Personal Attacks in Social Media Posts
Samuel Carton, Qiaozhu Mei, Paul Resnick
We introduce an adversarial method for producing high-recall explanations of neural text classifier decisions. Building on an existing architecture for extractive explanations via…
Context-aware Natural Language Generation with Recurrent Neural Networks
Jian Tang, Yifan Yang, Sam Carton +2
This paper studied generating natural languages at particular contexts or situations. We proposed two novel approaches which encode the contexts into a continuous semantic represen…