Open-Domain Hierarchical Event Schema Induction by Incremental Prompting and Verification

• URL: http://arxiv.org/abs/2307.01972v1
• Date: Wed, 5 Jul 2023 01:00:44 GMT
• Title: Open-Domain Hierarchical Event Schema Induction by Incremental Prompting and Verification
• Authors: Sha Li, Ruining Zhao, Manling Li, Heng Ji, Chris Callison-Burch, Jiawei Han
• Abstract summary: We treat event schemas as a form of commonsense knowledge that can be derived from large language models (LLMs) We design an incremental prompting and verification method to break down the construction of a complex event graph into three stages. Compared to directly using LLMs to generate a linearized graph, our method can generate large and complex schemas with 7.2% F1 improvement in temporal relations and 31.0% F1 improvement in hierarchical relations.
• Score: 81.17473088621209
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Event schemas are a form of world knowledge about the typical progression of events. Recent methods for event schema induction use information extraction systems to construct a large number of event graph instances from documents, and then learn to generalize the schema from such instances. In contrast, we propose to treat event schemas as a form of commonsense knowledge that can be derived from large language models (LLMs). This new paradigm greatly simplifies the schema induction process and allows us to handle both hierarchical relations and temporal relations between events in a straightforward way. Since event schemas have complex graph structures, we design an incremental prompting and verification method to break down the construction of a complex event graph into three stages: event skeleton construction, event expansion, and event-event relation verification. Compared to directly using LLMs to generate a linearized graph, our method can generate large and complex schemas with 7.2% F1 improvement in temporal relations and 31.0% F1 improvement in hierarchical relations. In addition, compared to the previous state-of-the-art closed-domain schema induction model, human assessors were able to cover $\sim$10% more events when translating the schemas into coherent stories and rated our schemas 1.3 points higher (on a 5-point scale) in terms of readability.

Related papers

• Prompt-based Graph Model for Joint Liberal Event Extraction and Event Schema Induction [1.3154296174423619]
  Events are essential components of speech and texts, describing the changes in the state of entities. The event extraction task aims to identify and classify events and find their participants according to event schemas. The researchers propose Liberal Event Extraction (LEE), which aims to extract events and discover event schemas simultaneously.
  arXiv  Detail & Related papers  (2024-03-19T07:56:42Z)
• Drafting Event Schemas using Language Models [48.81285141287434]
  We look at the process of creating such schemas to describe complex events. Our focus is on whether we can achieve sufficient diversity and recall of key events. We show that large language models are able to achieve moderate recall against schemas taken from two different datasets.
  arXiv  Detail & Related papers  (2023-05-24T07:57:04Z)
• Schema-adaptable Knowledge Graph Construction [47.772335354080795]
  Conventional Knowledge Graph Construction (KGC) approaches typically follow the static information extraction paradigm with a closed set of pre-defined schema. We propose a new task called schema-adaptable KGC, which aims to continually extract entity, relation, and event based on a dynamically changing schema graph without re-training.
  arXiv  Detail & Related papers  (2023-05-15T15:06:20Z)
• Zero-Shot On-the-Fly Event Schema Induction [61.91468909200566]
  We present a new approach 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. 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.
  arXiv  Detail & Related papers  (2022-10-12T14:37:00Z)
• Proton: Probing Schema Linking Information from Pre-trained Language Models for Text-to-SQL Parsing [66.55478402233399]
  We propose a framework to elicit relational structures via a probing procedure based on Poincar'e distance metric. Compared with commonly-used rule-based methods for schema linking, we found that probing relations can robustly capture semantic correspondences. Our framework sets new state-of-the-art performance on three benchmarks.
  arXiv  Detail & Related papers  (2022-06-28T14:05:25Z)
• Schema Curation via Causal Association Rule Mining [41.853476604903356]
  Event schemas are structured knowledge sources defining typical real-world scenarios. We present a framework for efficient human-in-the-loop construction of a schema library.
  arXiv  Detail & Related papers  (2021-04-18T10:48:26Z)
• Future is not One-dimensional: Graph Modeling based Complex Event Schema Induction for Event Prediction [90.75260063651763]
  We introduce the concept of Temporal Complex Event: a graph-based schema representation that encompasses events, arguments, temporal connections and argument relations. We release a new schema learning corpus containing 6,399 documents accompanied with event graphs, and manually constructed gold schemas.
  arXiv  Detail & Related papers  (2021-04-13T16:41:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.