A Neural Approach to Fine-Grained Argumentation Strategy Classification with Emotion and Moral Value Lexicons across Multiple Domains

2026-06-25 · Source: Paper Index on ACL Anthology · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, quick

Summary

A Neural Flair Transformer Classifier (NFTC) is introduced for fine-grained argumentation strategy classification, moving beyond basic claim/premise distinctions to identify specific persuasive tactics like using facts or values. The NFTC, built upon a fine-tuned RoBERTa document encoder, performs end-to-end argument component classification. It was evaluated across four diverse corpora, including public participation, persuasive forums, product reviews, and student essays. The NFTC consistently outperformed baselines such as majority-voting and Qwen2.5-7B, and also showed gains against a fine-tuned LLaMA-3-8B-Instruct model. The study also explored integrating auxiliary knowledge like emotion and moral value lexicon features. These features provided consistent performance gains in product reviews and persuasive forums, but their utility was found to be domain and schema dependent, showing mixed effects in other domains.

Key takeaway

For NLP Engineers developing fine-grained argumentation classifiers, you should consider the Neural Flair Transformer Classifier (NFTC) as a strong baseline, given its performance against LLaMA-3-8B-Instruct. When working with domains like product reviews or persuasive forums, integrate emotion and moral value lexicon features to potentially enhance classification accuracy. However, be aware that the utility of such subjective knowledge is highly domain-dependent, requiring careful evaluation for other contexts.

Key insights

Subjective knowledge, like emotion and moral value lexicons, offers domain-dependent gains in fine-grained argumentation classification.

Principles

• Fine-grained argument mining is challenging.
• Pre-trained LMs benefit argument classification.
• Auxiliary knowledge utility is domain-dependent.

Method

The Neural Flair Transformer Classifier (NFTC) fine-tunes a RoBERTa document encoder for end-to-end argument component classification, optionally integrating emotion and moral value lexicon features.

In practice

• Use RoBERTa for argument component classification.
• Consider emotion lexicons for product reviews.
• Apply moral value features in persuasive forums.

Topics

• Argument Mining
• Fine-Grained Classification
• RoBERTa
• Emotion Lexicons
• Moral Value Lexicons
• Natural Language Processing

Best for: Research Scientist, AI Scientist, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.