@inproceedings{yeghaneh-abkenar-etal-2026-neural,
title = "A Neural Approach to Fine-Grained Argumentation Strategy Classification with Emotion and Moral Value Lexicons across Multiple Domains",
author = "Yeghaneh Abkenar, Mohammad and
Wang, Weixing and
Stede, Manfred and
Romberg, Julia",
editor = "Elaraby, Mohamed and
Hautli-Janisz, Annette and
Romberg, Julia and
Musi, Elena and
Ruggeri, Federico and
Lawrence, John",
booktitle = "Proceedings of the 13th Workshop on Argument Mining and Reasoning",
month = jul,
year = "2026",
address = "San Diego, California, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.argmining-1.9/",
pages = "74--86",
ISBN = "979-8-89176-399-9",
abstract = "Fine-grained argumentation mining goes beyond coarse-grained distinctions such as claim and premise, by delving deeper into the underlying strategies employed (e.g., the use of facts or values to persuade the audience). Despite the advancements brought about by pre-trained language models, the task remains challenging. We investigate whether auxiliary knowledge such as emotion and moral value lexicon features can improve the classification of fine-grained argumentation strategies. Our Neural Flair Transformer Classifier (NFTC), in its base form, fine-tunes a transformer-based document encoder (RoBERTa) for end-to-end argument component classification. Evaluated across four corpora from diverse domains spanning public participation, persuasive forums, product reviews, and student essays, NFTC consistently outperforms majority-voting and Qwen2.5-7B baselines, achieving competitive performance on all datasets. Moreover, gains are observed against a fine-tuned LLaMA-3-8B-Instruct model, regarded in prior work as a leading approach. Injecting additional knowledge into NFTC yields mixed effects: emotion and moral value features provide consistent gains in product reviews and persuasive forums, but not in the other two domains. Our findings suggest that the utility of subjective knowledge is domain and schema dependent."
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<abstract>Fine-grained argumentation mining goes beyond coarse-grained distinctions such as claim and premise, by delving deeper into the underlying strategies employed (e.g., the use of facts or values to persuade the audience). Despite the advancements brought about by pre-trained language models, the task remains challenging. We investigate whether auxiliary knowledge such as emotion and moral value lexicon features can improve the classification of fine-grained argumentation strategies. Our Neural Flair Transformer Classifier (NFTC), in its base form, fine-tunes a transformer-based document encoder (RoBERTa) for end-to-end argument component classification. Evaluated across four corpora from diverse domains spanning public participation, persuasive forums, product reviews, and student essays, NFTC consistently outperforms majority-voting and Qwen2.5-7B baselines, achieving competitive performance on all datasets. Moreover, gains are observed against a fine-tuned LLaMA-3-8B-Instruct model, regarded in prior work as a leading approach. Injecting additional knowledge into NFTC yields mixed effects: emotion and moral value features provide consistent gains in product reviews and persuasive forums, but not in the other two domains. Our findings suggest that the utility of subjective knowledge is domain and schema dependent.</abstract>
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%0 Conference Proceedings
%T A Neural Approach to Fine-Grained Argumentation Strategy Classification with Emotion and Moral Value Lexicons across Multiple Domains
%A Yeghaneh Abkenar, Mohammad
%A Wang, Weixing
%A Stede, Manfred
%A Romberg, Julia
%Y Elaraby, Mohamed
%Y Hautli-Janisz, Annette
%Y Romberg, Julia
%Y Musi, Elena
%Y Ruggeri, Federico
%Y Lawrence, John
%S Proceedings of the 13th Workshop on Argument Mining and Reasoning
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-399-9
%F yeghaneh-abkenar-etal-2026-neural
%X Fine-grained argumentation mining goes beyond coarse-grained distinctions such as claim and premise, by delving deeper into the underlying strategies employed (e.g., the use of facts or values to persuade the audience). Despite the advancements brought about by pre-trained language models, the task remains challenging. We investigate whether auxiliary knowledge such as emotion and moral value lexicon features can improve the classification of fine-grained argumentation strategies. Our Neural Flair Transformer Classifier (NFTC), in its base form, fine-tunes a transformer-based document encoder (RoBERTa) for end-to-end argument component classification. Evaluated across four corpora from diverse domains spanning public participation, persuasive forums, product reviews, and student essays, NFTC consistently outperforms majority-voting and Qwen2.5-7B baselines, achieving competitive performance on all datasets. Moreover, gains are observed against a fine-tuned LLaMA-3-8B-Instruct model, regarded in prior work as a leading approach. Injecting additional knowledge into NFTC yields mixed effects: emotion and moral value features provide consistent gains in product reviews and persuasive forums, but not in the other two domains. Our findings suggest that the utility of subjective knowledge is domain and schema dependent.
%U https://aclanthology.org/2026.argmining-1.9/
%P 74-86
Markdown (Informal)
[A Neural Approach to Fine-Grained Argumentation Strategy Classification with Emotion and Moral Value Lexicons across Multiple Domains](https://aclanthology.org/2026.argmining-1.9/) (Yeghaneh Abkenar et al., ArgMining 2026)
ACL