CSECU - DSG at S em E val-2025 Task 6: Exploiting Multilingual Feature Fusion-based Approach for Corporate Promise Verification (2025)

Abstract: In SemEval-2025, we participated on the multilingual corporate promise verification task. In the task, we mainly focused on the promise and evidence identification task, and illustrated the performance for the five different languages. For all the languages, we proposed a unified state-of-the-art framework to classify the target labels. For the framework, we incorporated the pre-feature fusion approach, then integrate it with the neural network architecture. Additionally, in the dataset description and discussion section, we provide different insights of our finding through visualization of the dataset structures and explainability of the model’s performance.

## introduction in today's society, corporate, governmental, and public personalities' pledges have the power to shape public perception, influence stakeholder trust, and determine institutional reputation. these organizations' promises of social responsibility, environmental responsibility, and ethical governance are important markers of their legitimacy and accountability. however, the quantity and magnitude of such commitments make it extremely difficult to confirm whether they are actually being fulfilled. it is now more important than ever to be able to verify a promise, especially in the world of business, where corporations commonly make grand claims about their impact on environmental, social, and governance (esg) measures. in order to tackle this problem, semeval-2025 introduces ml-promise, the first multilingual dataset created especially for promise verification @xcite . this dataset allows for a cross-cultural analysis of corporate promises verifying four different evaluation criteria, described in in recent years, the application of natural language processing (nlp) in esg analysis and sustainability reporting has become increasingly common. for instance, gutierrez-bustamante et al. @xcite employed latent semantic analysis (lsa) and global vectors (glove) for word representation to assess the alignment of sustainability reports with the global reporting initiatives (gri) framework. @xcite ) employed text analysis of corporate social responsibility (csr) reports to identify reporting inconsistency between violator companies that violate environmental infractions and non-violator companies. moreover, the esgreveal system, introduced by @xcite , blends large language models (llms) with retrieval-augmented generation (rag) to retrieve structured esg details from esg reports. in this paper, we illustrate our insights accumulated from experimenting on this task. we proposed feature fusion based neural architecture, to identify the promise and evidence statements. here, we utilized the language-agnostic sentence representations (laser) and universal sentence encoder (use) embedding for the feature fusion. the structure of this paper is as follows: section 2 provides a detailed explanation of our proposed framework. in section 3, we present the experimental setup along with a comparative performance analysis. in section 4, we provide our insights and explanability of the models on the task. lastly, we conclude the paper in section 5, discussing poten- label description possible values promise identification does the statement contain a promise? yes / no supporting evidence is there evidence backing the claim? yes / no clarity of promise is the promise clear, unclear, or misleading? clear / not clear / misleading timeline verification when can the promise be verified? <2 years, 2-5 years, >5 years, other ## system overview we shape the corporate promise verification task as a sequence classification task and employ an ensemble of embedding models, laser, and use for the feature extraction process, then combine these two embedding layers, and feed them into the bi-lstm + mlp layer to get the desired label. the framework of our system is depicted in figure 1 . ## discussion to maintain clarity and focus, supplementary analyses-including topic modeling to identify key themes in corporate promises, sentiment analysis aimed at detecting greenwashing in esg reports, and word frequency analysis for promise detection-are provided in appendix 5. in this section, we focus on core evaluation aspects, including explainability analysis using shap and a detailed error analysis of the evidence identification task, to further interpret model performance and understand its limitations. ## conclusion and future work in this paper, we introduced a feature fusion based neural architecture framework for corporate promise verification. among them, for the feature fusion, we levaraged the feature embedding coming from laser and universal sentence encoder. then, the bi-lstm with mlp framework trained with after the feature integration to get the predictions for promise and evidence identification subtasks. our future plan amalgamates working with the clarity of promise and verification timeline subtasks, as well as focus on the better model by incorporating multilingual retrieval-augmented generation (rag) with large language model (llm) based framework for the corporate promise verification task. jianfeng deng, lianglun cheng, and zhuowei wang. 2021. attention-based bilstm fused cnn with gating mechanism model for chinese long text classification. computer speech & language, 68:101182. nina gorovaia and michalis makrominas. 2024. identifying greenwashing in corporate-social responsibility reports using natural-language processing. european financial management. marcelo gutierrez-bustamante and leonardo espinosa-leal. 2022. natural language processing methods for scoring sustainability reports-a study of nordic listed companies. sustainability, 14(15):9165. jing li, peizhang wang, lu jia, run mao, qian li, yongle he, yi sun, and pinwang zhao. 2023. design and implementation of an automated pdf drawing statistics tool based on python. in sixth international conference on computer information science and application technology (cisat 2023), volume 12800, pages 1686-1690. spie. gang liu and jiabao guo. 2019. bidirectional lstm with attention mechanism and convolutional layer for text classification. neurocomputing, 337:325-338. scott lundberg. 2017. a unified approach to interpreting model predictions. arxiv preprint arxiv:1705.07874. holger schwenk and matthijs douze. 2017. learning joint multilingual sentence representations with neural machine translation. arxiv preprint arxiv:1704.04154. holger schwenk and xian li. 2018. a corpus for multilingual document classification in eight languages. arxiv preprint arxiv:1805.09821. yunxiang zhang and zhuyi rao. 2020. n-bilstm: bilstm with n-gram features for text classification. in 2020 ieee 5th information technology and mechatronics engineering conference (itoec), pages 1056-1059. ieee. yi zou, mengying shi, zhongjie chen, zhu deng, zongxiong lei, zihan zeng, shiming yang, hongxiang tong, lei xiao, and wenwen zhou. 2025. esgreveal: an llm-based approach for extracting structured data from esg reports. journal of cleaner production, 489:144572.