Research - Papers
Explore a selection of our published work on a variety of key research challenges in AI.
Null It Out: Guarding Protected Attributes by Iterative Nullspace Projection
The ability to control for the kinds of information encoded in neural representation has a variety of use cases, especially in light of the challenge of interpreting these models. We present…
Obtaining Faithful Interpretations from Compositional Neural Networks
Neural module networks (NMNs) are a popular approach for modeling compositionality: they achieve high accuracy when applied to problems in language and vision, while reflecting the compositional…
pyBART: Evidence-based Syntactic Transformations for IE
Syntactic dependencies can be predicted with high accuracy, and are useful for both machine-learned and pattern-based information extraction tasks. However, their utility can be improved. These…
QuASE: Question-Answer Driven Sentence Encoding.
Question-answering (QA) data often encodes essential information in many facets. This paper studies a natural question: Can we get supervision from QA data for other tasks (typically, non-QA ones)?…
Recollection versus Imagination: Exploring Human Memory and Cognition via Neural Language Models
We investigate the use of NLP as a measure of the cognitive processes involved in storytelling, contrasting imagination and recollection of events. To facilitate this, we collect and release…
S2ORC: The Semantic Scholar Open Research Corpus
We introduce S2ORC, a large contextual citation graph of English-language academic papers from multiple scientific domains; the corpus consists of 81.1M papers, 380.5M citation edges, and associated…
SciREX: A Challenge Dataset for Document-Level Information Extraction
Extracting information from full documents is an important problem in many domains, but most previous work focus on identifying relationships within a sentence or a paragraph. It is challenging to…
Social Bias Frames: Reasoning about Social and Power Implications of Language
Language has the power to reinforce stereotypes and project social biases onto others. At the core of the challenge is that it is rarely what is stated explicitly, but all the implied meanings that…
SPECTER: Document-level Representation Learning using Citation-informed Transformers
Representation learning is a critical ingredient for natural language processing systems. Recent Transformer language models like BERT learn powerful textual representations, but these models are…
Stolen Probability: A Structural Weakness of Neural Language Models
Neural Network Language Models (NNLMs) generate probability distributions by applying a softmax function to a distance metric formed by taking the dot product of a prediction vector with all word…