VeriPromise ESG 2026

ESG Promise Verification Competition

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

Learn More

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 (Student Category)

🥇 First Place

Slots: 1 team
Prize: NTD 80,000
Certificate: Paper & Digital Certificate by Ministry of Education

🥈 Second Place

Slots: 1 team
Prize: NTD 50,000
Certificate: Paper & Digital Certificate by Ministry of Education

🥉 Third Place

Slots: 1 team
Prize: NTD 30,000
Certificate: Paper & Digital Certificate by Ministry of Education

🎖️ Excellence Awards

Slots: 2 teams
Prize: NTD 10,000
Certificate: Paper & Digital Certificate by Ministry of Education

🏅 Honorable Mentions

Slots: 10 teams
Prize: NTD 7,000
Certificate: Paper & Digital Certificate by Ministry of Education

※ Note: Industry professionals category receives digital certificates only (no prize money)
📌 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

March 1 - May 1, 2026
Registration Open & First Training Set Release

Team registration opens; first part of training dataset released

March 2026
Regional Tour Seminars

Competition briefing and technical sharing sessions

May 1 - June 1, 2026
Second Training Set Release

Second part of training dataset released

June 1 - June 15, 2026
Validation Set Release

Validation dataset provided for model tuning

June 18 - June 20, 2026
Test Set Release & Result Submission

Test set available 6/18; submission deadline 6/20 23:59:59

June 23, 2026
Preliminary Results Announcement

Initial competition results announced

June 23 - June 30, 2026
Report & Code Submission

Teams submit technical reports and implementation code

June 30 - July 14, 2026
Final Evaluation

Jury conducts final review and scoring

July 24, 2026
Final Rankings Announcement

Official announcement of final competition rankings

March 2027
Award Ceremony (Date TBA)

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
Website

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
Website

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
Website

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

FAQ

What is VeriPromise ESG?

VeriPromise ESG is an AI competition focused on verifying corporate sustainability commitments using NLP technology.

Who can participate?

The competition is open to students, researchers, and industry professionals. Teams can consist of 1-5 members.

What is the dataset?

The VeriPromiseESG4K dataset contains 4,000 annotated data points from Taiwan 50 Index constituent stocks' ESG reports.

標註範例說明

以下提供 E、S、G 三類文本的標註範例，協助參賽者理解標註規則與判斷標準

ESG 類別 E
類型 有承諾 - 有證據
段落內容
承諾遵循國際人權公約與基本勞動人權原則，落實性別平等、結社自由，並致力於消弭歧視與強迫勞動。
承諾狀態 是 (Yes)
證據狀態 是 (Yes)
證據品質 清晰 (Clear)
驗證時間軸 已執行 (already)
註解 有制度性承諾與明確人權框架。
ESG 類別 E
類型 有承諾 - 有證據
段落內容
除非資金明確用於綠能轉型計畫，不再新增投資燃煤比重超過 50% 的電廠；同時針對燃料煤相關產業制定嚴格准入與撤資標準，積極引導資金流向低碳與可再生能源領域。
承諾狀態 是 (Yes)
證據狀態 是 (Yes)
證據品質 不清晰 (Not Clear)
驗證時間軸 已執行 (already)
註解 行動方向明確，但量化成效與第三方驗證描述不足。
ESG 類別 E
類型 有承諾 - 無證據
段落內容
落實環境永續、實踐永續承諾是本公司的企業使命，將以 2050 年淨零排放為首要目標，致力實現多項環境永續承諾。
承諾狀態 是 (Yes)
證據狀態 否 (No)
證據品質 不適用 (N/A)
驗證時間軸 5 年以上 (more_than_5_years)
註解 屬於願景型承諾，未提供具體行動或成果。
ESG 類別 E
類型 無承諾
段落內容
應用人工智慧強化良率監控，輔助辦識模型有效性達 98%。
承諾狀態 否 (No)
證據狀態 不適用 (N/A)
證據品質 不適用 (N/A)
驗證時間軸 不適用 (N/A)
註解 僅敘述產品效果，未有行動承諾。
ESG 類別 S
類型 有承諾 - 有證據
段落內容
我們將持續推動零職災文化，完善承攬商納管與教育訓練機制，並加強現場稽核頻率，以降低高風險作業事故率。為此，我們正調整標準作業流程並提高稽核覆蓋率。
承諾狀態 是 (Yes)
證據狀態 是 (Yes)
證據品質 不清晰 (Not Clear)
驗證時間軸 2-5年 (between_2_and_5_years)
註解 明確表達企業將持續推動零職災文化，屬於清楚的行動承諾；相關行動未提供量化指標、明確頻率或制度細節，證據支撐力道有限。
ESG 類別 S
類型 有承諾 - 無證據
段落內容
制定企業內部衝突礦產管理聲明，承諾遵守 RBA 不使用衝突礦產政策。
承諾狀態 是 (Yes)
證據狀態 否 (No)
證據品質 不適用 (N/A)
驗證時間軸 已執行 (already)
註解 有制度性承諾與明確人權框架。
ESG 類別 S
類型 無承諾
段落內容
推出金來寶小額終身壽險，提供高齡者基本保險保障與終身壽險服務。
承諾狀態 否 (No)
證據狀態 不適用 (N/A)
證據品質 不適用 (N/A)
驗證時間軸 不適用 (N/A)
註解 僅是產品描述，未包含未來承諾語句。
ESG 類別 G
類型 有承諾 - 有證據
段落內容
我們將設立由三位獨立董事組成的永續委員會，每季檢視重大風險議題與對應計畫，並在年度股東會前完成報告揭露與外部查驗。
承諾狀態 是 (Yes)
證據狀態 是 (Yes)
證據品質 清晰 (Clear)
驗證時間軸 2-5年 (between_2_and_5_years)
註解 明確表達企業將設立永續委員會，並規劃其運作方式，屬於具體且可執行的治理承諾；清楚說明組織架構、執行頻率及查驗機制。
ESG 類別 G
類型 有承諾 - 有證據
段落內容
未來改選董事時將提高女性董事席次，以增進董事會性別多元化。
承諾狀態 是 (Yes)
證據狀態 是 (Yes)
證據品質 不清晰 (Not Clear)
驗證時間軸 2年內 (within_2_years)
註解 有明確方向，但缺乏具體目標與追蹤機制。
ESG 類別 G
類型 無承諾
段落內容
2024 年研發人員數達 446 人，完成研發專案 17 件，智慧財產權累計 202 件。
承諾狀態 否 (No)
證據狀態 不適用 (N/A)
證據品質 不適用 (N/A)
驗證時間軸 不適用 (N/A)
註解 營運成果描述，非治理承諾。

Contact Us

Feel free to reach out with any questions

Email

veripromiseesg2026@gmail.com

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

Related Resources

Important links to related competitions and research papers

PromiseEval-2025 @ SemEval

SemEval 2025 shared task on promise verification

Visit Website →

Multi-Lingual ESG Issue Identification

FNP@IJCAI-2023 paper on ESG issue identification

Read Paper →

Multi-Lingual ESG Impact Type Identification

FNP@IJCAI-2023 paper on ESG impact type classification

Read Paper →

Multi-Lingual ESG Impact Duration Inference

FNP@EMNLP-2024 paper on ESG impact duration prediction

Read Paper →

NTCIR-19 RegCom

NTCIR-19 Regulatory Compliance task

Visit Website →

ML-Promise: A Multilingual Dataset for Corporate Promise Verification

EMNLP 2025 paper on multilingual promise verification dataset

Read Paper →