Equip** 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 …

In the NLP community, recent years have seen a surge of research activities that address
machines' ability to perform deep language understanding which goes beyond what is …

Graphs arise naturally in many real-world applications including social networks,
recommender systems, ontologies, biology, and computational finance. Traditionally …

Understanding narratives requires reasoning about implicit world knowledge related to the
causes, effects, and states of situations described in text. At the core of this challenge is how …

Playing text-based games requires skills in processing natural language and sequential
decision making. Achieving human-level performance on text-based games remains an …

Query-based open-domain NLP tasks require information synthesis from long and diverse
web results. Current approaches extractively select portions of web text as input to …

Data-driven, knowledge-grounded neural conversation models are capable of generating
more informative responses. However, these models have not yet demonstrated that they …

Commonsense knowledge and commonsense reasoning are some of the main bottlenecks
in machine intelligence. In the NLP community, many benchmark datasets and tasks have …

Abstract Knowledge graphs represent relationships between entities. These graphs can take
dynamic forms to trace changes along time through text models and further used by …

The ability to generate clarification questions ie, questions that identify useful missing
information in a given context, is important in reducing ambiguity. Humans use previous …