Overview
The Fact Extraction and Verification (FEVER) workshop is set to take place at EMNLP-IJCNLP 2019, providing a platform for researchers to discuss advancements in fact-checking and information verification. This workshop aims to address the challenges posed by the vast amount of unstructured information available online, alongside the growing issue of misinformation.
Background & Relevance
In the current digital landscape, the ability to extract and verify facts from unstructured text is crucial. With an overwhelming amount of information available, structured sources are limited, making the transformation of free-form text into structured knowledge a significant challenge. The FEVER workshop seeks to tackle these issues and promote research in adversarial learning, which is becoming increasingly relevant in ensuring the reliability of information systems.
Key Details
- Submission Deadline (Extended): 30 August 2019
- Notification of Acceptance: 16 September 2019
- Camera-Ready Deadline: 30 September 2019
- Workshop Dates: 3-4 November 2019
- Location: EMNLP-IJCNLP 2019
- Submission Link: Softconf Submission
- Topics of Interest:
- Information Extraction
- Semantic Parsing
- Knowledge Base Population
- Natural Language Inference
- Textual Entailment Recognition
- Argumentation Mining
- Machine Reading and Comprehension
- Claim Validation/Fact Checking
- Question Answering
- Theorem Proving
- Stance Detection
- Adversarial Learning
- Computational Journalism
- System Demonstrations on the FEVER 2.0 Shared Task
Eligibility & Participation
The workshop welcomes submissions from researchers and practitioners in the fields of natural language processing, machine learning, and related areas. It is particularly relevant for those working on fact extraction, verification, and adversarial learning.
Submission or Application Guidelines
- Papers can be submitted as long (8 pages) or short (4 pages) papers, with unlimited pages for references.
- All submissions must be in PDF format and anonymized for review, adhering to the EMNLP 2019 two-column format.
- Non-archival submissions are allowed, which means content can be reused for other venues. Add “(NON-ARCHIVAL)” to the title for such submissions.
- Extended abstracts of up to 8 pages can also be submitted, marked with “(EXTENDED ABSTRACT)”. These will be presented as talks or posters if selected but will not be included in the proceedings.
Additional Context / Real-World Relevance
The importance of fact extraction and verification is underscored by the increasing prevalence of misinformation online. This workshop not only fosters academic discussion but also contributes to the development of practical solutions for real-world challenges in information reliability. The integration of adversarial learning into this domain is particularly timely, as it addresses the vulnerabilities of current systems against deceptive practices.
Conclusion
Researchers and practitioners are encouraged to participate in this workshop to share their insights and findings on fact extraction and verification. This is an excellent opportunity to contribute to a critical area of research that impacts the integrity of information in our digital age. Explore the submission guidelines and consider applying to be part of this important dialogue.
Category: CFP & Deadlines
Tags: fact-extraction, verification, nlp, emnlp, ijcnlp, adversarial-learning, information-extraction, knowledge-base, machine-learning
