Research - Papers
Explore a selection of our published work on a variety of key research challenges in AI.
Parsing with Multilingual BERT, a Small Treebank, and a Small Corpus
Pretrained multilingual contextual representations have shown great success, but due to the limits of their pretraining data, their benefits do not apply equally to all language varieties. This…
Plug and Play Autoencoders for Conditional Text Generation
Text autoencoders are commonly used for conditional generation tasks such as style transfer. We propose methods which are plug and play, where any pretrained autoencoder can be used, and only…
RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models
Pretrained neural language models (LMs) are prone to generating racist, sexist, or otherwise toxic language which hinders their safe deployment. We investigate the extent to which pretrained LMs can…
The Multilingual Amazon Reviews Corpus
We present the Multilingual Amazon Reviews Corpus (MARC), a large-scale collection of Amazon reviews for multilingual text classification. The corpus contains reviews in English, Japanese, German,…
TORQUE: A Reading Comprehension Dataset of Temporal Ordering Questions
A critical part of reading is being able to understand the temporal relationships between events described in a passage of text, even when those relationships are not explicitly stated. However,…
Writing Strategies for Science Communication: Data and Computational Analysis
Communicating complex scientific ideas without misleading or overwhelming the public is challenging. While science communication guides exist, they rarely offer empirical evidence for how their…
Evaluating Models' Local Decision Boundaries via Contrast Sets
Standard test sets for supervised learning evaluate in-distribution generalization. Unfortunately, when a dataset has systematic gaps (e.g., annotation artifacts), these evaluations are misleading:…
Break It Down: A Question Understanding Benchmark
Understanding natural language questions entails the ability to break down a question into the requisite steps for computing its answer. In this work, we introduce a Question Decomposition Meaning…
CORD-19: The Covid-19 Open Research Dataset
The Covid-19 Open Research Dataset (CORD-19) is a growing 1 resource of scientific papers on Covid-19 and related historical coronavirus research. CORD-19 is designed to facilitate the development…
A Formal Hierarchy of RNN Architectures
We develop a formal hierarchy of the expressive capacity of RNN architectures. The hierarchy is based on two formal properties: space complexity, which measures the RNN's memory, and rational…