Semantic representation is receiving growing attention in NLP in the past few years, and
many proposals for semantic schemes (eg, AMR, UCCA, GMB, UDS) have been put forth …

Abstract Representation and learning of commonsense knowledge is one of the
foundational problems in the quest to enable deep language understanding. This issue is …

We address the problem of automatically learning the main steps to complete a certain task,
such as changing a car tire, from a set of narrated instruction videos. The contributions of this …

Besides entity-centric knowledge, usually organized as Knowledge Graph (KG), events are
also an essential kind of knowledge in the world, which trigger the spring up of event-centric …

We present two latent variable models for learning character types, or personas, in film, in
which a persona is defined as a set of mixtures over latent lexical classes. These lexical …

In everyday life, humans often plan their actions by following step-by-step instructions in the
form of goal-oriented scripts. Previous work has exploited language models (LMs) to plan for …

We address the problem of automatically acquiring knowledge of event sequences from text,
with the aim of providing a predictive model for use in narrative generation systems. We …

Activity recognition has shown impressive progress in recent years. However, the
challenges of detecting fine-grained activities and understanding how they are combined …

Scripts encode knowledge of prototypical sequences of events. We describe a Recurrent
Neural Network model for statistical script learning using Long Short-Term Memory, an …

Story generation is the problem of automatically selecting a sequence of events that meet a
set of criteria and can be told as a story. Story generation is knowledge-intensive; traditional …