VeriPromise ESG 2026

🌍 ESG Promise Verification Competition

Leverage AI technology to verify corporate sustainability commitments and enhance ESG report transparency and credibility

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Competition Overview

In the era of global ESG (Environmental, Social, and Governance) focus, the authenticity and credibility of corporate sustainability reports are increasingly important. This competition aims to establish an automated sustainability commitment verification system using AI technology.

Full Name:VeriPromise ESG 2026 - ESG Promise Verification Competition

Objective:Develop an AI system capable of automatically identifying, analyzing, and verifying corporate sustainability commitments through four core tasks (promise recognition, evidence support, clarity assessment, and timeline prediction) to comprehensively evaluate the authenticity and credibility of ESG reports.
4
Competition Tasks
4,000
Annotated Data
50
Leading Companies
15
Industry Sectors
🎯

Practical Application

Address corporate 'Greenwashing' issues, enhance ESG report credibility, and help investors and stakeholders make more informed decisions.

🤖

Technical Challenge

Combine natural language processing, large language models, and multi-task learning to complete four challenge tasks: promise recognition, evidence linkage, clarity assessment, and timeline prediction.

🌏

International Collaboration

Co-hosted by top academic institutions from Taiwan and Japan, featured as an NTCIR-19 International Track project, providing high-quality multilingual datasets to promote global ESG AI research.

Competition Tasks

This competition comprises four core subtasks, covering the complete ESG report verification process from promise recognition to evidence assessment, clarity analysis, and timeline prediction

📌 Subtask 1: Promise Recognition

Objective:Determine whether a given sentence expresses a clear corporate commitment to future actions

Output Categories

  • Yes:Statements containing explicit commitments
  • No:General statements without commitments

Evaluation Metric

F1-Score (harmonic mean of precision and recall)

Examples

✅ Promise:"We commit to achieving carbon neutrality by 2030"
❌ Non-Promise:"We value the importance of environmental protection"

🔗 Subtask 2: Evidence Support Linkage

Objective:Determine whether identified promise statements are accompanied by specific action plans or supporting evidence

Output Categories

  • Yes:Promise has concrete evidence support
  • No:Promise lacks specific evidence

Evaluation Metric

F1-Score (semantic association judgment capability)

Example

Promise:"Promote low-carbon value chain transformation, continuously strengthen supplier energy-saving, carbon reduction, water conservation, and waste reduction guidance"
Evidence:"Require setting medium and long-term reduction targets and proposing specific actions" → Evidence Supported

💡 Subtask 3: Clarity Classification

Objective:Assess whether promise statements are semantically clear without ambiguous language, identifying potential 'greenwashing' risks

Output Categories

  • Clear:Semantically explicit and verifiable
  • Not Clear:Semantically ambiguous and difficult to quantify
  • Misleading:Potentially misleading statements

Evaluation Metric

Macro-F1 (average performance across three categories)

Practical Value

Help identify corporate 'greenwashing' behavior and enhance ESG report credibility

⏰ Subtask 4: Timeline Prediction

Objective:Infer the expected completion time of commitments based on statements to establish tracking mechanisms

Output Categories

  • Already:Commitment already fulfilled (verifiable in current period)
  • Within 2 years:Short-term target
  • Between 2 and 5 years:Medium-term target
  • More than 5 years:Long-term target

Evaluation Metric

Macro-F1 (four-category time inference capability)

🎯 Task Relevance and Practical Value

ESG Data Analyst

Corresponding Skills:Subtask 1
Key information extraction, text classification, sustainability report writing

Sustainable Investment Analyst

Corresponding Skills:Subtask 2
Semantic association judgment, logical reasoning, due diligence

Financial Regulatory Examiner

Corresponding Skills:Subtask 3
Greenwashing risk identification, semantic quality assessment, compliance audit

Corporate Sustainability Specialist

Corresponding Skills:Subtask 4
Time information extraction, goal management and tracking, project planning

Dataset Introduction

VeriPromiseESG4K - The world's first Traditional Chinese-designed sustainability commitment verification annotated dataset, sourced from Taiwan 50 Index constituents, spanning 15 industries' authentic ESG reports

📊 Dataset Features

Taiwan's Leading Companies

Real sustainability reports from Taiwan 50 Index (0050) constituent stocks, covering Taiwan's top 50 listed companies.

Cross-Industry Diversity

Spanning 15 different industry sectors including technology, finance, manufacturing, energy, etc., providing rich industry perspectives.

High-Quality Annotation

Executed in collaboration between National Taipei University and University of Taipei teams, with multi-stage quality control and Krippendorff's Alpha ensuring annotation consistency.

📈 Dataset Scale

Dataset Name:VeriPromiseESG4K (World's First Traditional Chinese Sustainability Commitment Verification Dataset)
Total Data Volume:4,000 high-quality annotated data points
Data Source:Taiwan 50 Index (0050) constituent stocks, covering top 50 listed companies
Industry Coverage:Spanning 15 industry sectors (technology, finance, manufacturing, energy, etc.)
Annotation Dimensions:Four subtasks (promise recognition, evidence support, clarity assessment, timeline verification)
Data Split:Training set + Test set (Public & Private)

🔍 Annotation Process

Phase 1: Initial Annotation

  • Professional annotation team performs initial marking
  • Establish annotation standards and guidelines
  • Conduct annotator training

Phase 2: Cross-Validation

  • Multiple annotators independently annotate
  • Calculate inter-annotator consistency
  • Resolve annotation discrepancies

Phase 3: Expert Review

  • Domain experts conduct final review
  • Quality control and corrections
  • Dataset release

Evaluation Methods

Adopt a multi-task comprehensive scoring mechanism to fully assess model performance across four subtasks

📊 Evaluation Metrics for Each Subtask

Subtask 1: Promise Recognition

Evaluation Metrics

  • F1-Score:Harmonic mean of precision and recall
  • Measures the model's ability to identify ESG promise statements

Subtask 2: Evidence Support Judgment

Evaluation Metrics

  • F1-Score:Determines whether promises have sufficient supporting evidence
  • Core practical capability assessment

Subtask 3: Clarity Assessment

Evaluation Metrics

  • Macro-F1:Three-category (clear/unclear/misleading) average performance
  • Most challenging task, identifying greenwashing risk capability

Subtask 4: Timeline Prediction

Evaluation Metrics

  • Macro-F1:Four-category time inference capability
  • Assesses model understanding of commitment timelines

🏅 Award Structure

🥇 First Place

Prize:NT$ 100,000
Certificate:Issued by Ministry of Education

🥈 Second Place

Prize:NT$ 60,000
Certificate:Issued by Ministry of Education

🥉 Third Place

Prize:NT$ 30,000
Certificate:Issued by Ministry of Education

🎖️ Merit Awards

Slots:12 teams
Certificate:Issued by Ministry of Education

📌 Ranking Rules:
• Final ranking based on Private Dataset test results
• Public Dataset for reference during competition only
• Top 25% teams exceeding Baseline receive Program Office digital certificates

Competition Schedule

November 2025 - January 2026
Data Collection and Annotation

Collect Taiwan corporate ESG reports, conduct professional annotation and quality control

February - March 2026
Competition Promotion and Registration

Announce competition details, dataset, and evaluation methods; open team registration

April - May 2026
Training Dataset Release and Development Phase

Provide 4,000 training data points; participants develop and optimize models

Mid-June 2026
Test Dataset Release and Submission

Test set available 6/18; submission deadline 6/20 23:59:59 (3 submissions per day)

July 2026
Result Evaluation and Review

Jury reviews submitted results, technical reports, and code

August 2026
Results Announcement and Award Ceremony

Announce winners, hold award ceremony and technical sharing session

Organizing Team

Co-hosted by top academic institutions from Taiwan and Japan with industry experts

🎓 Principal Investigator

Prof. Min-Yuh Day

Prof. Min-Yuh Day

Principal Investigator
National Taipei University
Graduate Institute of Information Management

Specialized in artificial intelligence, generative AI, and sustainable green fintech. Currently Director of Fintech and Green Finance Research Center.

👥 Co-Principal Investigators

Dr. Chung-Chi Chen

Dr. Chung-Chi Chen

Co-Principal Investigator
National Institute of Advanced Industrial Science and Technology (AIST)
Artificial Intelligence Research Center

Founder of ACL SIG-FinTech, specialized in financial opinion mining and natural language processing.

Prof. Yohei Seki

Prof. Yohei Seki

Co-Principal Investigator
University of Tsukuba
Faculty of Library, Information and Media Science

Specialized in natural language processing and information retrieval, organized NTCIR multilingual opinion analysis tasks.

👨‍💼 Research Assistants

Hsin-Ting LU

Hsin-Ting LU

Researcher
🔗 Website
Wen-Ze Chen

Wen-Ze Chen

Researcher
🔗 Website
Wei-Chun Huang

Wei-Chun Huang

Researcher
🔗 Website
Yu-Han Huang

Yu-Han Huang

Researcher
🔗 Website
Jun-Yu Wu

Jun-Yu Wu

Researcher
🔗 Website

🏢 Partner Institutions

National Taipei University

Graduate Institute of Information Management
Fintech and Green Finance Research Center

University of Taipei

Department of Computer Science

National Institute of Advanced Industrial Science and Technology (AIST)

Artificial Intelligence Research Center

University of Tsukuba

Faculty of Library, Information and Media Science

Data Examples

Sample annotated data from TSMC's ESG sustainability report

ESG Type: E
Data:
台積公司除致力落實領先業界的溫室氣體減量標竿作為，包括製程端加速汰換與新設現址式處理設備、使用碳中和天然氣、打造符合綠建築認證的綠色廠房、執行機台與廠務節能專案並擴大使用再生能源等
Promise Status: Yes
Promise String: 台積公司除致力落實領先業界的溫室氣體減量標竿作為
Timeline: Already
Evidence Status: Yes
Evidence String: 包括製程端加速汰換與新設現址式處理設備、使用碳中和天然氣、打造符合綠建築認證的綠色廠房、執行機台與廠務節能專案並擴大使用再生能源等
Evidence Quality: Clear
Source: Page 97
ESG Type: S
Data:
台積公司重視營建工地安全，積極建置在地優先、以人為本的工作場域，堅持聘用當地國籍的從業人員，自新建工程階段，即與施工承攬商、工地安全衛生委員會、安全管理中心成立工地安全管理組織，以完善的三級管理制度共同嚴守施工環境
Promise Status: Yes
Promise String: 台積公司重視營建工地安全，積極建置在地優先、以人為本的工作場域，堅持聘用當地國籍的從業人員
Timeline: Already
Evidence Status: Yes
Evidence String: 自新建工程階段，即與施工承攬商、工地安全衛生委員會、安全管理中心成立工地安全管理組織，以完善的三級管理制度共同嚴守施工環境
Evidence Quality: Clear
Source: Page 178
ESG Type: G
Data:
台積公司以「建立規範、評核機制與合作、多元推廣、風險管理」四大面向提升供應鏈資安防護強度，民國111年已完成659家供應商資安評鑑，其中有481家獲得A級、326家於6個月內提升1-2個資安等級
Promise Status: Yes
Promise String: 台積公司以「建立規範、評核機制與合作、多元推廣、風險管理」四大面向提升供應鏈資安防護強度
Timeline: Already
Evidence Status: Yes
Evidence String: 民國111年已完成659家供應商資安評鑑，其中有481家獲得A級、326家於6個月內提升1-2個資安等級
Evidence Quality: Clear
Source: Page 214
ESG Type: G
Data:
本公司董事會10位成員具備多元背景，包括不同產業、學術及法律等專業背景，國內及歐美不同國籍，擁有世界級公司經營經驗，其中並包含1名女性董事；10位董事中，6位為獨立董事，占全體董事席次60%，且董事間無具有配偶及二親等以內親屬關係之情形，因此本公司董事會具有獨立性。
Promise Status: No
Promise String: -
Timeline: N/A
Evidence Status: N/A
Evidence String: -
Evidence Quality: N/A
Source: Page 206
ESG Type: S
Data:
台積公司將持續編寫《新建廠工程作業環安衛藍皮書》，內容分為九大章節，共計90份作業管理項目，預計於民國112年發行，做為台灣營造業提升施工安全的作業指引
Promise Status: Yes
Promise String: 台積公司將持續編寫《新建廠工程作業環安衛藍皮書》...預計於民國112年發行，做為台灣營造業提升施工安全的作業指引
Timeline: Within 2 years
Evidence Status: Yes
Evidence String: 內容分為九大章節，共計90份作業管理項目
Evidence Quality: Not Clear
Source: Page 179
ESG Type: G
Data:
為讓董事會更加完備透明,成就更完善的公司治理,我們也在這一年籌備董事會其下的「提名及公司治理暨永續委員會」,進一步確保董事會永續治理績效,由台積公司董事會於民國112年2月核准。
Promise Status: Yes
Promise String: 為讓董事會更加完備透明,成就更完善的公司治理,我們也在這一年籌備董事會其下的「提名及公司治理暨永續委員會」,進一步確保董事會永續治理績效,由台積公司董事會於民國112年2月核准。
Timeline: Within 2 years
Evidence Status: No
Evidence String: -
Evidence Quality: N/A
Source: Page 4
ESG Type: S
Data:
次年度工作目標● 持續提升綠色製造績效，拓展資源循環行動，建立生物多樣性策略與行動，穩定邁向淨零排放目標
Promise Status: Yes
Promise String: 持續提升綠色製造績效，拓展資源循環行動，建立生物多樣性策略與行動，穩定邁向淨零排放目標
Timeline: Within 2 years
Evidence Status: No
Evidence String: -
Evidence Quality: N/A
Source: Page 16
ESG Type: S
Data:
策略: 釋放多元人才潛能 民國119年目標: 女性主管占比達20% 民國111年成果: 女性主管占比為13.3% 目標：14%
Promise Status: Yes
Promise String: 女性主管占比達20%
Timeline: More than 5 years
Evidence Status: Yes
Evidence String: 民國111年成果: 女性主管占比為13.3% 目標：14%
Evidence Quality: Misleading
Source: Page 135

Contact Us

Feel free to reach out with any questions

📧

Email

[email protected]

🌐

Official Website

National Taipei University

💻

Sample Code

Google Drive

💬

Competition Platform

AI CUP Registration System

📢 Important Reminders:
• Register via AI CUP Registration System (https://go.aicup.tw/)
• Teams consist of 1-5 members; no changes after registration
• Test set submission period: 6/18/2026 11:00 - 6/20/2026 23:59:59 (3 submissions per day)
• Submit technical report, implementation code, and environment documentation
• Top 15 all-student teams receive Ministry of Education certificates
• External data and pre-trained models allowed; detailed disclosure required in report