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Research - Papers

Explore a selection of our published work on a variety of key research challenges in AI.

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“I’m Not Mad”: Commonsense Implications of Negation and Contradiction

Liwei JiangAntoine BosselutChandra BhagavatulaYejin Choi
2021
NAACL

Natural language inference requires reasoning about contradictions, negations, and their commonsense implications. Given a simple premise (e.g., “I’m mad at you”), humans can reason about the… 

Learning Curves for Analysis of Deep Networks

Derek HoiemTanmay GuptaZhizhong LiMichal Shlapentokh-Rothman
2021
arXiv

A learning curve models a classifier's test error as a function of the number of training samples. Prior works show that learning curves can be used to select model parameters and extrapolate… 

Visual Semantic Role Labeling for Video Understanding

Arka SadhuTanmay GuptaMark YatskarAniruddha Kembhavi
2021
CVPR

We propose a new framework for understanding and representing related salient events in a video using visual semantic role labeling. We represent videos as a set of related events, wherein each… 

Visual Room Rearrangement

Luca WeihsMatt DeitkeAniruddha KembhaviR. Mottaghi
2021
arXiv

There has been a significant recent progress in the field of Embodied AI with researchers developing models and algorithms enabling embodied agents to navigate and interact within completely unseen… 

LayoutParser: A Unified Toolkit for Deep Learning Based Document Image Analysis

Zejiang ShenRuochen ZhangMelissa DellWeining Li
2021
arXiv

Recent advances in document image analysis (DIA) have been primarily driven by the application of neural networks. Ideally, research outcomes could be easily deployed in production and extended for… 

Thinking Aloud: Dynamic Context Generation Improves Zero-Shot Reasoning Performance of GPT-2

G. BetzKyle RichardsonChristian Voigt
2021
arXiv

Thinking aloud is an effective meta-cognitive strategy human reasoners apply to solve difficult problems. We suggest to improve the reasoning ability of pre-trained neural language models in a… 

Information to Wisdom: Commonsense Knowledge Extraction and Compilation

Simon RazniewskiNiket TandonAparna S. Varde
2021
WSDM '21: Proceedings of the 14th ACM International Conference on Web Search and Data Mining

Commonsense knowledge is a foundational cornerstone of artificial intelligence applications. Whereas information extraction and knowledge base construction for instance-oriented assertions, such as… 

What Can You Learn from Your Muscles? Learning Visual Representation from Human Interactions

Kiana EhsaniDaniel GordonT. NguyenA. Farhadi
2021
ICLR

Learning effective representations of visual data that generalize to a variety of downstream tasks has been a long quest for computer vision. Most representation learning approaches rely solely on… 

Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AI

Alon JacoviAna MarasovićTim MillerYoav Goldberg
2021
FAccT

Trust is a central component of the interaction between people and AI, in that 'incorrect' levels of trust may cause misuse, abuse or disuse of the technology. But what, precisely, is the nature of… 

Gender trends in computer science authorship

Lucy Lu WangGabriel StanovskyLuca WeihsOren Etzioni
2021
CACM

A comprehensive and up-to-date analysis of Computer Science literature (2.87 million papers through 2018) reveals that, if current trends continue, parity between the number of male and female…