E n C laim: A Style Augmented Transformer Architecture for Environmental Claim Detection (2024)

Abstract: Across countries, a noteworthy paradigm shift towards a more sustainable and environmentally responsible economy is underway. However, this positive transition is accompanied by an upsurge in greenwashing, where companies make exaggerated claims about their environmental commitments. To address this challenge and protect consumers, initiatives have emerged to substantiate green claims. With the proliferation of environmental and scientific assertions, a critical need arises for automated methods to detect and validate these claims at scale. In this paper, we introduce EnClaim, a transformer network architecture augmented with stylistic features for automatically detecting claims from open web documents or social media posts. The proposed model considers various linguistic stylistic features in conjunction with language models to predict whether a given statement constitutes a claim. We have rigorously evaluated the model using multiple open datasets. Our initial findings indicate that incorporating stylistic vectors alongside the BERT-based language model enhances the overall effectiveness of environmental claim detection.

## introduction amid the ongoing climate crisis, a remarkable shift is taking place towards establishing a more sustainable and environmentally responsible economy. this transition is primarily being propelled by evolving regulations, shifting public sentiments, and changing attitudes among investors. however, this promising shift has been accompanied by a surge in greenwashing, with companies making exaggerated claims about their environmental commitments foot_0 . such environmental advertisements can also mislead consumers due to vague or false claims, thereby harming brand or product outcomes. to address this challenge and protect type texts (0/1) 1 a total population of 6148 is getting the benefit of safe potable drinking water due to this initiative. 0 our ambition is to be the preferred energy company for all stakeholders, and we have a disciplined three-phase strategy to meet that ambition. 1 says gop primary opponents glenn grothman and joe leibham cast a com-promise vote that cost $788 million in higher electricity costs. 0 says the annies list political group supports third-trimester abortions on demand. recent advancements in large-scale language models such as transformers, and gpt have influenced researchers to apply such techniques for claim detection tasks. the advantage of such techniques lies in the fact that multi-layer neural networks can automatically learn complex language structures. however, such deep neural network architectures are yet to take into account integral linguistic factors present in the text, which play an important role in determining claim-type statements. moreover, existing approaches to claim detection seldom consider the deep stylistic features embedded within the text that can play an important role in the classification task accordingly, in this paper, we propose a stylistically enhanced transformer-based architecture for the automatic classification of statements into "claim" or "not a claim" statements. our model considers stylistic feature embeddings along with the standard transformer-based language model. to our knowledge, no prior work in this field has investigated the effectiveness of combining the above factors for environmental claim detection tasks. our preliminary investigation shows that the incorporation of stylistic feature vectors along with the language model does improve the overall performance of the classification model and it is not only limited to environmental claim detection. regardless of the dataset, our proposed architecture empowers claim detection leveraging stylistic fingerprints within sentences. ## related work a plethora of studies have been done on the analysis of environmental fake news, and corporate greenwashing. recent endeavors on building computational models include climatebert @xcite , and @xcite , two language models pre-trained on climate-related text. nlp tasks and datasets include climate change topic detection @xcite and detecting media stance on global warming @xcite . @xcite ), detecting media stance on global warming @xcite ), collecting climate change opinions at scale from social platforms @xcite ), and finally, the analysis of regulatory disclosures @xcite ; @xcite ). claim spotting is the task of finding fact-check worthy claims @xcite ; @xcite ; @xcite ). pledge detection aims to detect pledges made in, for example, political campaigns @xcite ; @xcite ). to the best of our knowledge, we have not encountered any approach that extensively makes use of the deep linguistic and stylistic factors for the identification of claimworthy sentences. plex linguistic, grammatical, and stylistic features associated with a text document connected to a bert-based language model network. the overall architecture of the model is depicted in figure 1 . we will begin the model architecture by first explaining about generating the grammatical and stylistic feature embeddings that will in turn be used by the transformer architecture. ## conclusion in this paper, we present a style-aware transformer architecture for environmental claim detection. while conventional deep neural networks, including cnn and lstm, have historically struggled to discern the intricate relationships among various grammatical and stylistic elements that play a pivotal role in assessing text quality, our method takes a different approach and considers the distinct stylistic features such as vagueness, conviction and commitment with the power of pre-trained transformer models. these features have proven to be indispensable in the accurate evaluation of environmental claims. we have compared the performance of the proposed model with the state-of-theart open-source llms including finetuned mistral model and llama-2. we have observed across different datasets. enclaim surpasses most of the texts enclaim llama-2 article 2 of the agree-correct wrong ment also aims to ensure that finance flows are consistent with low carbon impact, climate resilient devlopment. can new jersey continue wrong correct to afford to pay for a 0 emissions energy? historically, we have backed wrong wrong out significant adjustments to the environmental texts enclaim f. mistral and then we're doing as correct correct much we can to offset the labor-related expenses. can new jersey continue correct hallucination to afford to pay for a 0 emissions energy? our future work focuses on expanding the granularity of our environmental claim detection model. we aim to categorize claims into subcategories like pollution or resource use, and further identify specific environmental aspects impacted. this can be achieved through a hierarchical taxonomy, domainspecific knowledge integration, and named entity recognition techniques. furthermore, multi-label classification and sentiment analysis can offer a richer understanding of claims' complexity and potential impact. by pursuing these directions, we can empower our model to provide more granular and impactful insights into environmental claims, ultimately contributing to informed decision-making and progress toward sustainability.