Rich Event Modeling for Script Event Prediction

• URL: http://arxiv.org/abs/2212.08287v1
• Date: Fri, 16 Dec 2022 05:17:59 GMT
• Title: Rich Event Modeling for Script Event Prediction
• Authors: Long Bai, Saiping Guan, Zixuan Li, Jiafeng Guo, Xiaolong Jin, Xueqi Cheng
• Abstract summary: We propose the Rich Event Prediction (REP) framework for script event prediction. REP contains an event extractor to extract such information from texts. The core component of the predictor is a transformer-based event encoder to flexibly deal with an arbitrary number of arguments.
• Score: 60.67635412135682
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Script is a kind of structured knowledge extracted from texts, which contains a sequence of events. Based on such knowledge, script event prediction aims to predict the subsequent event. To do so, two aspects should be considered for events, namely, event description (i.e., what the events should contain) and event encoding (i.e., how they should be encoded). Most existing methods describe an event by a verb together with only a few core arguments (i.e., subject, object, and indirect object), which are not precise. In addition, existing event encoders are limited to a fixed number of arguments, which are not flexible to deal with extra information. Thus, in this paper, we propose the Rich Event Prediction (REP) framework for script event prediction. Fundamentally, it is based on the proposed rich event description, which enriches the existing ones with three kinds of important information, namely, the senses of verbs, extra semantic roles, and types of participants. REP contains an event extractor to extract such information from texts. Based on the extracted rich information, a predictor then selects the most probable subsequent event. The core component of the predictor is a transformer-based event encoder to flexibly deal with an arbitrary number of arguments. Experimental results on the widely used Gigaword Corpus show the effectiveness of the proposed framework.

Related papers

• Improving Event Definition Following For Zero-Shot Event Detection [66.27883872707523]
  Existing approaches on zero-shot event detection usually train models on datasets annotated with known event types. We aim to improve zero-shot event detection by training models to better follow event definitions.
  arXiv  Detail & Related papers  (2024-03-05T01:46:50Z)
• Towards Event Extraction from Speech with Contextual Clues [61.164413398231254]
  We introduce the Speech Event Extraction (SpeechEE) task and construct three synthetic training sets and one human-spoken test set. Compared to event extraction from text, SpeechEE poses greater challenges mainly due to complex speech signals that are continuous and have no word boundaries. Our method brings significant improvements on all datasets, achieving a maximum F1 gain of 10.7%.
  arXiv  Detail & Related papers  (2024-01-27T11:07:19Z)
• EventGraph: Event Extraction as Semantic Graph Parsing [5.21480688623047]
  Event extraction involves the detection and extraction of both the event triggers and corresponding event arguments. We propose EventGraph, a joint framework for event extraction, which encodes events as graphs. Our code and models are released as open-source.
  arXiv  Detail & Related papers  (2022-10-16T22:11:46Z)
• Rethinking the Event Coding Pipeline with Prompt Entailment [1.370633147306388]
  For monitoring crises, political events are extracted from the news. The large amount of unstructured full-text event descriptions makes a case-by-case analysis unmanageable. We propose PR-ENT, a new event coding approach that is more flexible and resource-efficient.
  arXiv  Detail & Related papers  (2022-10-11T08:38:48Z)
• Unifying Event Detection and Captioning as Sequence Generation via Pre-Training [53.613265415703815]
  We propose a unified pre-training and fine-tuning framework to enhance the inter-task association between event detection and captioning. Our model outperforms the state-of-the-art methods, and can be further boosted when pre-trained on extra large-scale video-text data.
  arXiv  Detail & Related papers  (2022-07-18T14:18:13Z)
• Integrating Deep Event-Level and Script-Level Information for Script Event Prediction [60.67635412135681]
  We propose a Transformer-based model, called MCPredictor, which integrates deep event-level and script-level information for script event prediction. The experimental results on the widely-used New York Times corpus demonstrate the effectiveness and superiority of the proposed model.
  arXiv  Detail & Related papers  (2021-09-24T07:37:32Z)
• EventPlus: A Temporal Event Understanding Pipeline [12.313545429119651]
  EventPlus is a temporal event understanding pipeline that integrates various state-of-the-art event understanding components. We make EventPlus publicly available to facilitate event-related information extraction and downstream applications.
  arXiv  Detail & Related papers  (2021-01-13T08:00:50Z)
• Unsupervised Label-aware Event Trigger and Argument Classification [73.86358632937372]
  We propose an unsupervised event extraction pipeline, which first identifies events with available tools (e.g., SRL) and then automatically maps them to pre-defined event types. We leverage pre-trained language models to contextually represent pre-defined types for both event triggers and arguments. We successfully map 83% of the triggers and 54% of the arguments to the correct types, almost doubling the performance of previous zero-shot approaches.
  arXiv  Detail & Related papers  (2020-12-30T17:47:24Z)

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.