Chandra Bhagavatula

We propose a general semi-supervised approach for adding pre-trained context embeddings from bidirectional language models to NLP systems and apply it to sequence labeling tasks. Expand

We present ATOMIC, an atlas of everyday commonsense reasoning, organized through 877k textual descriptions of inferential knowledge about events described in natural language and associated with typed if-then relations. Expand

We present TabEL, a new EL system that performs the Entity Linking task on phrases in cells of Web tables. Expand

We present WikiTables, a Web application that enables users to interactively explore tabular knowledge extracted from Wikipedia by automatically joining together disparate tables to uncover "interesting" relationships. Expand

We introduce WinoGrande, a large-scale dataset of 44k problems, inspired by the original WSC design, but adjusted to improve both the scale and the hardness of the dataset. Expand

We present a content-based method for recommending citations in an academic paper draft. Expand

We describe a deployed scalable system for organizing published scientific literature into a heterogeneous graph to facilitate algorithmic manipulation and discovery. Expand

We introduce Cosmos QA, a large-scale dataset of 35,600 problems that require commonsense-based reading comprehension, formulated as multiple-choice questions that require reasoning beyond the exact text spans in the context. Expand

This paper describes our submission for the ScienceIE shared task (SemEval2017 Task 10) on entity and relation extraction from scientific papers. Expand

In this paper, we present the first study that investigates the viability of language-based abductive reasoning. Expand