Research - Papers
Explore a selection of our published work on a variety of key research challenges in AI.
Scruples: A Corpus of Community Ethical Judgments on 32, 000 Real-Life Anecdotes
As AI systems become an increasing part of people's everyday lives, it becomes ever more important that they understand people's ethical norms. Motivated by descriptive ethics, a field of study that…
GENIE: A Leaderboard for Human-in-the-Loop Evaluation of Text Generation
Leaderboards have eased model development for many NLP datasets by standardizing their evaluation and delegating it to an independent external repository. Their adoption, however, is so far limited…
On-the-Fly Attention Modularization for Neural Generation
Despite considerable advancements with deep neural language models (LMs), neural text generation still suffers from degeneration: generated text is repetitive, generic, selfinconsistent, and lacking…
VinVL: Revisiting Visual Representations in Vision-Language Models
This paper presents a detailed study of improving visual representations for vision language (VL) tasks and develops an improved object detection model to provide object-centric representations of…
CLUE: A Chinese Language Understanding Evaluation Benchmark
We introduce CLUE, a Chinese Language Understanding Evaluation benchmark. It contains eight different tasks, including single-sentence classification, sentence pair classification, and machine…
Edited Media Understanding: Reasoning About Implications of Manipulated Images
Multimodal disinformation, from `deepfakes' to simple edits that deceive, is an important societal problem. Yet at the same time, the vast majority of media edits are harmless -- such as a filtered…
Text mining approaches for dealing with the rapidly expanding literature on COVID-19
More than 50 000 papers have been published about COVID-19 since the beginning of 2020 and several hundred new papers continue to be published every day. This incredible rate of scientific…
Belief Propagation Neural Networks
Learned neural solvers have successfully been used to solve combinatorial optimization and decision problems. More general counting variants of these problems, however, are still largely solved with…
Leap-Of-Thought: Teaching Pre-Trained Models to Systematically Reason Over Implicit Knowledge
To what extent can a neural network systematically reason over symbolic facts? Evidence suggests that large pre-trained language models (LMs) acquire some reasoning capacity, but this ability is…
Learning About Objects by Learning to Interact with Them
Much of the remarkable progress in computer vision has been focused around fully supervised learning mechanisms relying on highly curated datasets for a variety of tasks. In contrast, humans often…