CLaC at SemEval-2025 Task 6: A Multi-Architecture Approach for Corporate Environmental Promise Verification

TL;DR

This paper introduces a multi-architecture approach for verifying corporate promises in ESG reports, combining transformer models, linguistic features, and multi-task learning to improve accuracy on promise verification tasks.

Contribution

It proposes three novel model architectures, including a combined subtask model with attention and multi-objective learning, advancing promise verification in ESG reports.

Findings

Progressive performance improvement across models

Combined subtask model outperforms baseline

Effective use of linguistic features and attention mechanisms

Abstract

This paper presents our approach to the SemEval-2025 Task~6 (PromiseEval), which focuses on verifying promises in corporate ESG (Environmental, Social, and Governance) reports. We explore three model architectures to address the four subtasks of promise identification, supporting evidence assessment, clarity evaluation, and verification timing. Our first model utilizes ESG-BERT with task-specific classifier heads, while our second model enhances this architecture with linguistic features tailored for each subtask. Our third approach implements a combined subtask model with attention-based sequence pooling, transformer representations augmented with document metadata, and multi-objective learning. Experiments on the English portion of the ML-Promise dataset demonstrate progressive improvement across our models, with our combined subtask approach achieving a leaderboard score of 0.5268, outperforming the provided baseline of 0.5227. Our work highlights the effectiveness of linguistic feature extraction, attention pooling, and multi-objective learning in promise verification tasks, despite challenges posed by class imbalance and limited training data.