Workshop Description
In recent years, there has been a significant increase in the amount of publicly generated digital data. One prominent category of this data, and arguably the largest in terms of daily generation, pertains to various real-world events, ranging from natural disasters to political occurrences to sports events. Detecting these events serves various crucial purposes, including early warning systems, emergency response, situational awareness, tracking public health trends, and understanding societal shifts, among others. However, automatic real-time event detection presents intriguing challenges, primarily stemming from the characteristics of the data. These challenges include the diversity of public online data (multimodal nature), the rapid pace at which data is produced (velocity), the sheer volume of data generated, and the reliability of the data (veracity). Moreover, the recent advancements in powerful Large Language Models (LLMs) and Generative AI Systems offer new opportunities to revise event detection pipelines, enabling novel approaches and applications across various domains. The workshop focuses on:
- Looking forward and looking back: The workshop will solicit ideas on how the field of event detection should evolve over the next twenty years, as well as solicit papers reflecting on what has worked and not worked in the field thus far.
- Expanding Beyond NLP: As noted above, there are many sibling areas that actively research event detection. Many of these areas have remained siloed and there is not much cross communication though they are working on similar problem areas. This workshop seeks to address this by actively soliciting research and invited speakers from these areas.
- Theory to Application: Finally, this workshop will emphasize how event detection technology can be used in real-world applications.
We will solicit novel papers, including, but not limited to the following topics:
- Position and opinion papers on the state and future of event detection
- Retrospectives
- Multimodal event detection
- Large language models (LLMs) and their applications for event detection and related areas
- Event detection on non-traditional sources of data
- Inferring causal, temporal, coreference, and sub-event relations for events
- Multilingual event detection
- Event representation
- Event ontology
- Never-ending learning
- Streaming algorithms for event detection
- Interpretability of event detection methods
- Bias detection and mitigation
- Human-AI Interaction for event detection framework
- Information visualization for events
- Anomaly detection
- Practical application of event detection for different domains such as emergency response
- Usability of event detection systems
- Datasets for Event Detection
Important Dates
All deadlines are 11:59 pm UTC-12 (anywhere on Earth).
- Submission Deadline: Thursday, August 15
- Notification of Acceptance: Friday, September 20
- Camera Ready Deadline: Friday, October 4
- Workshop: either November 15 or 16
Submission Information
We will be using the EMNLP Submission Guidelines for the workshop. Authors are invited to submit a full paper of up to 8 pages of content with unlimited pages for references. We also invite short papers of up to 4 pages of content, including unlimited pages for references. Final camera ready versions of accepted papers will be given an additional page of content to address reviewer comments.
Previously published papers cannot be accepted. The submissions will be reviewed by the program committee. As reviewing will be blind, please ensure that papers are anonymous. Self-references that reveal the author's identity, e.g., "We previously showed (Smith, 1991) ...", should be avoided. Instead, use citations such as "Smith previously showed (Smith, 1991) ...".
Please note that unlike EMNLP, which uses ARR for submission management, we will be using the START conference system. The link will be made live when available.
https://softconf.com/emnlp2024/FuturED/
Organizing Committee
- Joel Tetreault, Dataminr
- Thien Huu Nguyen, University of Oregon
- Hemank Lamba, Dataminr
- Amanda Hughes, Brigham Young University
Contact Information
- Workshop contact email address: futureofeventdetection [at] googlegroup.com
- Workshop Twitter: @FuturED2024