Research - Papers
Explore a selection of our published work on a variety of key research challenges in AI.
Event2Mind: Commonsense Inference on Events, Intents, and Reactions
We investigate a new commonsense inference task: given an event described in a short free-form text (“X drinks coffee in the morning”), a system reasons about the likely intents (“X wants to stay…
Extending a Parser to Distant Domains Using a Few Dozen Partially Annotated Examples
We revisit domain adaptation for parsers in the neural era. First we show that recent advances in word representations greatly diminish the need for domain adaptation when the target domain is…
Imagine This! Scripts to Compositions to Videos
Imagining a scene described in natural language with realistic layout and appearance of entities is the ultimate test of spatial, visual, and semantic world knowledge. Towards this goal, we present…
IQA: Visual Question Answering in Interactive Environments
We introduce Interactive Question Answering (IQA), the task of answering questions that require an autonomous agent to interact with a dynamic visual environment. IQA presents the agent with a scene…
Learning to Write with Cooperative Discriminators
Despite their local fluency, long-form text generated from RNNs is often generic, repetitive, and even self-contradictory. We propose a unified learning framework that collectively addresses all the…
LSTMs Exploit Linguistic Attributes of Data
While recurrent neural networks have found success in a variety of natural language processing applications, they are general models of sequential data. We investigate how the properties of natural…
Modeling Naive Psychology of Characters in Simple Commonsense Stories
Understanding a narrative requires reading between the lines and reasoning about the unspoken but obvious implications about events and people’s mental states — a capability that is trivial for…
Simple and Effective Multi-Paragraph Reading Comprehension
We consider the problem of adapting neural paragraph-level question answering models to the case where entire documents are given as input. Our proposed solution trains models to produce well…
Transferring Common-Sense Knowledge for Object Detection
We propose the idea of transferring common-sense knowledge from source categories to target categories for scalable object detection. In our setting, the training data for the source categories have…
Ultra-Fine Entity Typing
We introduce a new entity typing task: given a sentence with an entity mention, the goal is to predict a set of free-form phrases (e.g. skyscraper, songwriter, or criminal) that describe appropriate…