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 …

The information age enables people to obtain news online through various channels, yet in
the meanwhile making false news spread at unprecedented speed. Fake news exerts …

Entities are at the center of how we represent and aggregate knowledge. For instance,
Encyclopedias such as Wikipedia are structured by entities (eg, one per Wikipedia article) …

Contextual word representations, typically trained on unstructured, unlabeled text, do not
contain any explicit grounding to real world entities and are often unable to remember facts …

This paper introduces a conceptually simple, scalable, and highly effective BERT-based
entity linking model, along with an extensive evaluation of its accuracy-speed trade-off. We …

We introduce CM3, a family of causally masked generative models trained over a large
corpus of structured multi-modal documents that can contain both text and image tokens …

We present a neural approach called IRNet for complex and cross-domain Text-to-SQL.
IRNet aims to address two challenges: 1) the mismatch between intents expressed in natural …

We contribute the largest publicly available dataset of naturally occurring factual claims for
the purpose of automatic claim verification. It is collected from 26 fact checking websites in …

This paper shows how to construct knowledge graphs (KGs) from pre-trained language
models (eg, BERT, GPT-2/3), without human supervision. Popular KGs (eg, Wikidata, NELL) …

An enormous amount of digital information is expressed as natural-language (NL) text that is
not easily processable by computers. Knowledge Graphs (KG) offer a widely used format for …