Research - Papers
Explore a selection of our published work on a variety of key research challenges in AI.
Explaining NLP Models via Minimal Contrastive Editing (MiCE)
Humans give contrastive explanations that explain why an observed event happened rather than some other counterfactual event (the contrast case). Despite the important role that contrastivity plays…
Explaining Relationships Between Scientific Documents
We address the task of explaining relationships between two scientific documents using natural language text. This task requires modeling the complex content of long technical documents, deducing a…
Few-Shot Question Answering by Pretraining Span Selection
In a number of question answering (QA) benchmarks, pretrained models have reached human parity through fine-tuning on an order of 100,000 annotated questions and answers. We explore the more…
How effective is BERT without word ordering? Implications for language understanding and data privacy
Ordered word sequences contain the rich structures that define language. However, it’s often not clear if or how modern pretrained language models utilize these structures. We show that the token…
Neural Extractive Search
Domain experts often need to extract structured information from large corpora. We advocate for a search paradigm called “extractive search”, in which a search query is enriched with capture-slots,…
PAWLS: PDF Annotation With Labels and Structure
Adobe’s Portable Document Format (PDF) is a popular way of distributing view-only documents with a rich visual markup. This presents a challenge to NLP practitioners who wish to use the information…
PIGLeT: Language Grounding Through Neuro-Symbolic Interaction in a 3D World
We propose PIGLeT: a model that learns physical commonsense knowledge through interaction, and then uses this knowledge to ground language. We factorize PIGLeT into a physical dynamics model, and a…
Promoting Graph Awareness in Linearized Graph-to-Text Generation
Generating text from structured inputs, such as meaning representations or RDF triples, has often involved the use of specialized graphencoding neural networks. However, recent applications of…
Quality at a Glance: An Audit of Web-Crawled Multilingual Datasets
With the success of large-scale pre-training and multilingual modeling in Natural Language Processing (NLP), recent years have seen a proliferation of large, web-mined text datasets covering…
Shortformer: Better Language Modeling using Shorter Inputs
We explore the benefits of decreasing the input length of transformers. First, we show that initially training the model on short subsequences, before moving on to longer ones, both reduces overall…