@inproceedings{feger-dietze-2024-bertweets,
title = "{BERT}weet{'}s {TACO} Fiesta: Contrasting Flavors On The Path Of Inference And Information-Driven Argument Mining On {T}witter",
author = "Feger, Marc and
Dietze, Stefan",
editor = "Duh, Kevin and
Gomez, Helena and
Bethard, Steven",
booktitle = "Findings of the Association for Computational Linguistics: NAACL 2024",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-naacl.146",
pages = "2256--2266",
abstract = "Argument mining, dealing with the classification of text based on inference and information, denotes a challenging analytical task in the rich context of Twitter (now $\mathbb{X}$), a key platform for online discourse and exchange. Thereby, Twitter offers a diverse repository of short messages bearing on both of these elements. For text classification, transformer approaches, particularly BERT, offer state-of-the-art solutions. Our study delves into optimizing the embeddings of the understudied BERTweet transformer for argument mining on Twitter and broader generalization across topics.We explore the impact of pre-classification fine-tuning by aligning similar manifestations of inference and information while contrasting dissimilar instances. Using the TACO dataset, our approach augments tweets for optimizing BERTweet in a Siamese network, strongly improving classification and cross-topic generalization compared to standard methods.Overall, we contribute the transformer WRAPresentations and classifier WRAP, scoring 86.62{\%} F1 for inference detection, 86.30{\%} for information recognition, and 75.29{\%} across four combinations of these elements, to enhance inference and information-driven argument mining on Twitter.",
}
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<abstract>Argument mining, dealing with the classification of text based on inference and information, denotes a challenging analytical task in the rich context of Twitter (now \mathbbX), a key platform for online discourse and exchange. Thereby, Twitter offers a diverse repository of short messages bearing on both of these elements. For text classification, transformer approaches, particularly BERT, offer state-of-the-art solutions. Our study delves into optimizing the embeddings of the understudied BERTweet transformer for argument mining on Twitter and broader generalization across topics.We explore the impact of pre-classification fine-tuning by aligning similar manifestations of inference and information while contrasting dissimilar instances. Using the TACO dataset, our approach augments tweets for optimizing BERTweet in a Siamese network, strongly improving classification and cross-topic generalization compared to standard methods.Overall, we contribute the transformer WRAPresentations and classifier WRAP, scoring 86.62% F1 for inference detection, 86.30% for information recognition, and 75.29% across four combinations of these elements, to enhance inference and information-driven argument mining on Twitter.</abstract>
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%0 Conference Proceedings
%T BERTweet’s TACO Fiesta: Contrasting Flavors On The Path Of Inference And Information-Driven Argument Mining On Twitter
%A Feger, Marc
%A Dietze, Stefan
%Y Duh, Kevin
%Y Gomez, Helena
%Y Bethard, Steven
%S Findings of the Association for Computational Linguistics: NAACL 2024
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F feger-dietze-2024-bertweets
%X Argument mining, dealing with the classification of text based on inference and information, denotes a challenging analytical task in the rich context of Twitter (now \mathbbX), a key platform for online discourse and exchange. Thereby, Twitter offers a diverse repository of short messages bearing on both of these elements. For text classification, transformer approaches, particularly BERT, offer state-of-the-art solutions. Our study delves into optimizing the embeddings of the understudied BERTweet transformer for argument mining on Twitter and broader generalization across topics.We explore the impact of pre-classification fine-tuning by aligning similar manifestations of inference and information while contrasting dissimilar instances. Using the TACO dataset, our approach augments tweets for optimizing BERTweet in a Siamese network, strongly improving classification and cross-topic generalization compared to standard methods.Overall, we contribute the transformer WRAPresentations and classifier WRAP, scoring 86.62% F1 for inference detection, 86.30% for information recognition, and 75.29% across four combinations of these elements, to enhance inference and information-driven argument mining on Twitter.
%U https://aclanthology.org/2024.findings-naacl.146
%P 2256-2266
Markdown (Informal)
[BERTweet’s TACO Fiesta: Contrasting Flavors On The Path Of Inference And Information-Driven Argument Mining On Twitter](https://aclanthology.org/2024.findings-naacl.146) (Feger & Dietze, Findings 2024)
ACL