1. Abstract

Brief summary of your paper in a few sentences.

2. Introduction

• Task description and importance; mention language(s) covered; cite task overview paper
• Your main system strategy
• Key findings, ranking, challenges discovered; include code URL if available (Highly recommended)

3. Background

• Summarize task setup: input/output types (with examples)
• Dataset details: language, genre, size
• Tracks participated in (if applicable)
• Cite relevant related work showing your contribution's novelty

4. System Overview

• Key algorithms and design decisions
• Resources used beyond provided training data
• How you addressed task challenges
• Include equations/pseudocode for novel approaches
• Provide concrete algorithm examples
• For multiple systems, clearly distinguish each configuration

5. Experimental Setup

• Data split usage (train/dev/test)
• Preprocessing and hyperparameter details for replication
• External tools/libraries (with versions and URLs)
• Task evaluation metrics summary
• Put detailed implementation in Appendix if space-limited

6. Results

• Quantitative findings: Official metrics performance and ranking
• Analysis: Ablations, comparisons, design decision impact
• Error analysis: System mistakes, confusion matrices, error types, examples
• Clearly mark which data split used for each analysis
• Distinguish official vs. post-submission results

7. Conclusion

Summary of system, limitations, results, and future work directions.

8. Acknowledgments

Thank contributors, grants, anonymous reviewers.

9. Appendix

Low-level details for replication that aren't essential for understanding main concepts.

Templates and Style

• Must use: Official EMNLP 2025 (ACL) style templates (LaTeX or Word)
• Cite task overview paper
• Download from: https://github.com/acl-org/ACLPUB
• Follow: General "ACL" conference formatting guidelines
• Do NOT: Modify style files or use templates from other conferences to avoid the paper rejection

Consequences

Non-conforming submissions (wrong paper size, margins, font size) will be rejected without review.

Key Principles

• Focus on:
  • Replicability: Provide all necessary implementation details
  • Analysis: Emphasize results over rankings; include multiple runs and ablations
  • Clarity: Brief task outline (avoid duplicating task paper); use placeholder citations initially
• Required elements:
  • Cite task overview paper
  • Follow EMNLP templates exactly
  • Use title format: <Team Name> at <Task Name>: <Your Contribution>
  • For popular algorithms: Citation suffices (no need for full mathematical details)
  • Space management: Move detailed parameters/hyperparameters to Appendix if needed