Research - Papers
Explore a selection of our published work on a variety of key research challenges in AI.
Benchmarking Hierarchical Script Knowledge
Understanding procedural language requires reasoning about both hierarchical and temporal relations between events. For example, “boiling pasta” is a sub-event of “making a pasta dish”, typically…
Neural network gradient-based learning of black-box function interfaces
Deep neural networks work well at approximating complicated functions when provided with data and trained by gradient descent methods. At the same time, there is a vast amount of existing functions…
FlowQA: Grasping Flow in History for Conversational Machine Comprehension
Conversational machine comprehension requires a deep understanding of the conversation history. To enable traditional, single-turn models to encode the history comprehensively, we introduce Flow, a…
Visual Semantic Navigation using Scene Priors
How do humans navigate to target objects in novel scenes? Do we use the semantic/functional priors we have built over years to efficiently search and navigate? For example, to search for mugs, we…
The Curious Case of Neural Text Degeneration
Despite considerable advances in neural language modeling, it remains an open question what the best decoding strategy is for text generation from a language model (e.g. to generate a story). The…
Tactical Rewind: Self-Correction via Backtracking in Vision-And-Language Navigation
We present the Frontier Aware Search with backTracking (FAST) Navigator, a general framework for action decoding, that achieves state-of-the-art results on the 2018 Room-to-Room (R2R)…
DREAM: A Challenge Data Set and Models for Dialogue-Based Reading Comprehension
We present DREAM, the first dialogue-based multiple-choice reading comprehension data set. Collected from English as a Foreign Language examinations designed by human experts to evaluate the…
ATOMIC: An Atlas of Machine Commonsense for If-Then Reasoning
We present ATOMIC, an atlas of everyday commonsense reasoning, organized through 877k textual descriptions of inferential knowledge. Compared to existing resources that center around taxonomic…
QuaRel: A Dataset and Models for Answering Questions about Qualitative Relationships
Many natural language questions require recognizing and reasoning with qualitative relationships (e.g., in science, economics, and medicine), but are challenging to answer with corpus-based methods.…
Declarative Question Answering over Knowledge Bases containing Natural Language Text with Answer Set Programming
While in recent years machine learning (ML) based approaches have been the popular approach in developing end-to-end question answering systems, such systems often struggle when additional knowledge…