Zero-Shot On-the-Fly Event Schema Induction

Findings of the Association for Computational Linguistics: EACL 2023, pages 705–725 May 2-6, 2023 ©2023 Association for Computational Linguistics Zero-Shot On-the-Fly Event Schema Induction Rotem Dror∗, Haoyu Wang∗, and Dan Roth Department of Computer and Information Science University of Pennsylvania {rtmdrr,why16gzl,danroth}@seas.upenn.edu Abstract What are the events involved in a pandemic outbreak? What steps should be taken when planning a wedding? The answers to these questions can be found by collecting many documents on the complex event of interest, extracting relevant information, and analyzing it. We present a new approach1 in which large language models are utilized to gener- ate source documents that allow predicting, given a high-level event definition, the specific events, arguments, and relations between them to construct a schema that describes the com- plex event in its entirety. Using our model, complete schemas on any topic can be gener- ated on-the-fly without any manual data collec- tion, i.e., in a zero-shot manner. Moreover, we develop efficient methods to extract pertinent information from texts and demonstrate in a series of experiments that these schemas are considered to be more complete than human- curated ones in the majority of examined sce- narios. Finally, we show that this framework is comparable in performance with previous su- pervised schema induction methods that rely on collecting real texts and even reaching the best score in the prediction task. 1 Introduction Event processing refers to tracking, analyzing, and drawing conclusions from streams of information about events. This event analysis aims at identi- fying meaningful events (such as opportunities or threats) in real-time situations and responding ap- propriately. Event processing can also be utilized to gain a deep understanding of the specific steps, arguments, and relations between them that are in- volved in a complex event. The information above can be consolidated into a graphical representation called an event schema (Li et al., 2021). For in- stance in Fig. 1, the graph representation of events ∗Indicating equal contribution. 1https://cogcomp.seas.upenn.edu/page/ publication_view/995 and participants assists in gaining an understanding of the complex event of kidnapping and could help composing a reaction plan if needed. The NLP community has devoted much effort to understanding events that are described in a docu- ment or in a collection of documents for this pur- pose. These efforts include identifying event trig- gers (Lu and Roth, 2012; Huang et al., 2018; Wad- den et al., 2019; Han et al., 2019), extracting event arguments (Punyakanok et al., 2008; Peng et al., 2016; Lin et al., 2020; Zhang et al., 2021a), and pre- dicting the relations between events, e.g., temporal, coreferential, causal or hierarchical relations (Do et al., 2012; Lee et al., 2012; Glavaš et al., 2014; Ning et al., 2018; Wang et al., 2020; Zhang et al., 2020a; Trong et al., 2022). Previous works on event schema induction re- lied on the information extracted from manually collected documents to build the schema graph. For instance, Li et al. (2020) learn an auto-regressive language model (LM) over paths in the instance graphs depicting events, …

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