Research - Papers
Explore a selection of our published work on a variety of key research challenges in AI.
MS2: Multi-Document Summarization of Medical Studies
To assess the effectiveness of any medical intervention, researchers must conduct a timeintensive and highly manual literature review. NLP systems can help to automate or assist in parts of this…
Achieving Model Robustness through Discrete Adversarial Training
Discrete adversarial attacks are symbolic perturbations to a language input that preserve the output label but lead to a prediction error. While such attacks have been extensively explored for the…
What's in your Head? Emergent Behaviour in Multi-Task Transformer Models
The primary paradigm for multi-task training in natural language processing is to represent the input with a shared pre-trained language model, and add a small, thin network (head) per task. Given…
Moral Stories: Situated Reasoning about Norms, Intents, Actions, and their Consequences
In social settings, much of human behavior is governed by unspoken rules of conduct. For artificial systems to be fully integrated into social environments, adherence to such norms is a central…
proScript: Partially Ordered Scripts Generation
Scripts standardized event sequences describing typical everyday activities have been shown to help understand narratives by providing expectations, resolving ambiguity, and filling in unstated…
Contrastive Explanations for Model Interpretability
Contrastive explanations clarify why an event occurred in contrast to another. They are more inherently intuitive to humans to both produce and comprehend. We propose a methodology to produce…
Back to Square One: Bias Detection, Training and Commonsense Disentanglement in the Winograd Schema
The Winograd Schema (WS) has been proposed as a test for measuring commonsense capabilities of models. Recently, pre-trained language model-based approaches have boosted performance on some WS…
Documenting Large Webtext Corpora: A Case Study on the Colossal Clean Crawled Corpus
As language models are trained on ever more text, researchers are turning to some of the largest corpora available. Unlike most other types of datasets in NLP, large unlabeled text corpora are often…
Generative Context Pair Selection for Multi-hop Question Answering
Compositional reasoning tasks like multi-hop question answering, require making latent decisions to get the final answer, given a question. However, crowdsourced datasets often capture only a slice…
Learning with Instance Bundles for Reading Comprehension
When training most modern reading comprehension models, all the questions associated with a context are treated as being independent from each other. However, closely related questions and their…