Research - Papers
Explore a selection of our published work on a variety of key research challenges in AI.
Visual Reaction: Learning to Play Catch with Your Drone
In this paper we address the problem of visual reaction: the task of interacting with dynamic environments where the changes in the environment are not necessarily caused by the agents itself.…
RoboTHOR: An Open Simulation-to-Real Embodied AI Platform
Visual recognition ecosystems (e.g. ImageNet, Pascal, COCO) have undeniably played a prevailing role in the evolution of modern computer vision. We argue that interactive and embodied visual AI has…
Use the Force, Luke! Learning to Predict Physical Forces by Simulating Effects
When we humans look at a video of human-object interaction, we can not only infer what is happening but we can even extract actionable information and imitate those interactions. On the other hand,…
Do Dogs have Whiskers? A New Knowledge Base of hasPart Relations
We present a new knowledge-base (KB) of hasPart relationships, extracted from a large corpus of generic statements. Complementary to other resources available, it is the first which is all three of:…
When Bert Forgets How To POS: Amnesic Probing of Linguistic Properties and MLM Predictions
A growing body of work makes use of probing in order to investigate the working of neural models, often considered black boxes. Recently, an ongoing debate emerged surrounding the limitations of the…
Analysis of the Penn Korean Universal Dependency Treebank (PKT-UD): Manual Revision to Build Robust Parsing Model in Korean
In this paper, we first open on important issues regarding the Penn Korean Universal Treebank (PKT-UD) and address these issues by revising the entire corpus manually with the aim of producing…
Contextual Word Representations: Putting Words into Computers
This article aims to tell the story of how we put words into computers. It is part of the story of the field of natural language processing (NLP), a branch of artificial intelligence.a It targets a…
On Consequentialism and Fairness
Recent work on fairness in machine learning has primarily emphasized how to define, quantify, and encourage "fair" outcomes. Less attention has been paid, however, to the ethical foundations which…
TREC-COVID: Constructing a Pandemic Information Retrieval Test Collection
TREC-COVID is a community evaluation designed to build a test collection that captures the information needs of biomedical researchers using the scientific literature during a pandemic. One of the…
TREC-COVID: Rationale and Structure of an Information Retrieval Shared Task for COVID-19
TREC-COVID is an information retrieval (IR) shared task initiated to support clinicians and clinical research during the COVID-19 pandemic. IR for pandemics breaks many normal assumptions, which can…