用多架构模型验证企业环保承诺真伪，性能优于基线
CLaC at SemEval-2025 Task 6: A Multi-Architecture Approach for Corporate Environmental Promise Verification
- 融合语言特征与注意力池化的多任务模型
- 在英文数据集上达到0.5268的领先得分
- 适合可持续投资与ESG审计场景使用
本文介绍我们在SemEval-2025任务6（PromiseEval）中的方法，聚焦于企业ESG报告中承诺的验证。针对承诺识别、支持证据评估、清晰度评价和验证时间四个子任务，我们探索了三种模型架构：第一种基于ESG-BERT并附加任务特定分类头；第二种在该架构基础上引入各子任务定制的语言特征；第三种采用联合子任务模型，结合注意力序列池化、增强文档元数据的Transformer表示及多目标学习。在ML-Promise数据集英文部分的实验表明，模型性能逐步提升，最终组合模型获得0.5268的排行榜分数，优于提供的基线0.5227。研究证实语言特征提取、注意力池化和多目标学习在承诺验证任务中的有效性，尽管面临类别不平衡和训练数据有限的挑战。
原文摘要 · Abstract (English)
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.
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