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 generate 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 complex event in its entirety. Using our model, complete schemas on any topic can be generated on-the-fly without any manual data collection, 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 scenarios. Finally, we show that this framework is comparable in performance with previous supervised schema induction methods that rely on collecting real texts and even reaching the best score in the prediction task.
|Title of host publication||EACL 2023 - 17th Conference of the European Chapter of the Association for Computational Linguistics, Findings of EACL 2023|
|Publisher||Association for Computational Linguistics (ACL)|
|Number of pages||21|
|State||Published - 2023|
|Event||17th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2023 - Findings of EACL 2023 - Dubrovnik, Croatia|
Duration: 2 May 2023 → 6 May 2023
|Name||EACL 2023 - 17th Conference of the European Chapter of the Association for Computational Linguistics, Findings of EACL 2023|
|Conference||17th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2023 - Findings of EACL 2023|
|Period||2/05/23 → 6/05/23|
Bibliographical notePublisher Copyright:
© 2023 Association for Computational Linguistics.
ASJC Scopus subject areas
- Computational Theory and Mathematics
- Linguistics and Language