Large, pre-trained language models (PLMs) such as BERT and GPT have drastically
changed the Natural Language Processing (NLP) field. For numerous NLP tasks …

Equipping machines with comprehensive knowledge of the world's entities and their
relationships has been a longstanding goal of AI. Over the last decade, large-scale …

While pretrained models such as BERT have shown large gains across natural language
understanding tasks, their performance can be improved by further training the model on a …

We present the results of the Machine Reading for Question Answering (MRQA) 2019
shared task on evaluating the generalization capabilities of reading comprehension …

We present data and methods that enable a supervised learning approach to Open
Information Extraction (Open IE). Central to the approach is a novel formulation of Open IE …

Ambiguity is inherent to open-domain question answering; especially when exploring new
topics, it can be difficult to ask questions that have a single, unambiguous answer. In this …

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 …

Despite significant progress in text generation models, a serious limitation is their tendency
to produce text that is factually inconsistent with information in the input. Recent work has …

Event extraction has long been a challenging task, addressed mostly with supervised
methods that require expensive annotation and are not extensible to new event ontologies …

Existing datasets for natural language inference (NLI) have propelled research on language
understanding. We propose a new method for automatically deriving NLI datasets from the …