Event-Centric Natural Language Processing (2021)

Abstract: This tutorial targets researchers and practitioners who are interested in AI technologies that help machines understand natural language text, particularly real-world events described in the text. These include methods to extract the internal structures of an event regarding its protagonist(s), participant(s) and properties, as well as external structures concerning memberships, temporal and causal relations of multiple events. This tutorial will provide audience with a systematic introduction of (i) knowledge representations of events, (ii) various methods for automated extraction, conceptualization and prediction of events and their relations, (iii) induction of event processes and properties, and (iv) a wide range of NLU and commonsense understanding tasks that benefit from aforementioned techniques. We will conclude the tutorial by outlining emerging research problems in this area.

## introduction human languages always involve the description of real-world events. therefore, understanding events plays a critical role in nlp. for example, narrative prediction benefits from learning the causal relations of events to predict what happens next in a story @xcite ; machine comprehension of documents may involve understanding of events that affect the stock market @xcite , describe natural phenomena @xcite or identify disease phenotypes @xcite . in fact, event understanding also widely finds its important use cases in tasks such as opendomain question answering @xcite , intent prediction @xcite , timeline construction @xcite , text summarization @xcite and misinformation detection @xcite . since events are not just simple, standalone predicates, frontier research on event understanding generally faces two key challenges. one challenge is to precisely induce the relations of events, which describe memberships, co-reference, temporal orders and causality of events. the other is to comprehend the inherent structure and properties of an event, concerning its participants, granularity, location and time. in this tutorial, we will comprehensively review existing paradigms for event-centric knowledge representation in literature, and focus on their contributions to nlp tasks. beyond introducing partial-label and unsupervised learning approaches for event extraction, we will discuss recent constrained learning and structured inference approaches for multi-faceted event-event relation extraction from text. we will also review recent data-driven methods for event prediction tasks, including event process induction and conceptualization, and how an event-centric language model benefits narrative prediction. in addition, we will illustrate how distantly supervised approaches help resolve temporal and causal commonsense understanding of events, and how they can be applied to construct a large-scale eventuality knowledge base. participants will learn about recent trends and emerging challenges in this topic, representative tools and learning resources to obtain ready-to-use models, and how related models and techniques benefit end-use nlp applications. ## outline of tutorial content this half-day tutorial presents a systematic overview of recent advances in event-centric nlp technologies. we will begin with motivating this topic with several real-world applications, and introduce the main research problems. then, we will introduce methods for automated extraction of events as well as their participants, properties and relations from natural language text. based on the extracted eventuality knowledge, we will explain how various prediction tasks, including the completion of an event complex, conceptualization and consolidation of event processes, can be resolved. we will also discuss commonsense understanding of events, with a focus on the temporal and cognitive aspects. moreover, we will exemplify the use of aforementioned technologies in nlp applications of various domains, and will outline emerging research challenges that may catalyze further investigation on this topic. the detailed contents are outlined below. ## specification of the tutorial the proposed tutorial is considered a cuttingedge tutorial that introduces the recent advances in an emerging area of nlp. the presented topic has not been covered by previous acl/emnlp/naacl/eacl/coling tutorials in the past 4 years. this tutorial has not been presented elsewhere, while a more ai-flavored version with a subset of the contents has been planned in parallel at aaai 2021, to be presented by a subset of the instructors. we estimate that at least 60% of the works covered in this tutorial are from researchers other than the instructors.