Conference-acceptance-rate is a data collection that tracks acceptance rates across top-tier AI conferences, primarily in natural language processing and computer vision.
The project addresses the need for researchers to understand submission competitiveness and trends in major academic venues. It compiles acceptance rate statistics for conferences including ACL, EMNLP, NAACL-HLT, and COLING, breaking down rates by paper category where applicable such as long papers, short papers, and findings tracks. The data is presented in tabular format with acceptance counts and percentages, supplemented by trend visualizations.
Researchers planning submissions to major AI conferences will find this useful for calibrating expectations about acceptance likelihood and observing how selectivity has evolved over time. The tool suits anyone conducting meta-analysis of conference trends or needing historical context on venue competitiveness. The project covers natural language processing and computational linguistics conferences extensively, with computer vision and pattern recognition conferences also included.
The project shows sparse development activity with incomplete data entries for some conference years and tracks acceptance rates across multiple conference editions over an extended period.
