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    <abstract>Measuring advances in argument mining is one of the main challenges in the area. Different theories of argument, heterogeneous annotations, and a varied set of argumentation domains make it difficult to contextualise and understand the results reported in different work from a general perspective. In this paper, we present ARIES, a general benchmark for Argument Relation Identification aimed at providing with a standard evaluation for argument mining research. ARIES covers the three different language modelling approaches: sequence and token modelling, and sequence-to-sequence-to-sequence alignment, together with the three main Transformer-based model architectures: encoder-only, decoder-only, and encoder-decoder. Furthermore, the benchmark consists of eight different argument mining datasets, covering the most common argumentation domains, and standardised with the same annotation structures. This paper provides a first comprehensive and comparative set of results in argument mining across a broad range of configurations to compare with, both advancing the state-of-the-art, and establishing a standard way to measure future advances in the area. Across varied task setups and architectures, our experiments reveal consistent challenges in cross-dataset evaluation, with notably poor results. Given the models’ struggle to acquire transferable skills, the task remains challenging, opening avenues for future research.</abstract>
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%0 Conference Proceedings
%T ARIES: A General Benchmark for Argument Relation Identification
%A Gemechu, Debela
%A Ruiz-Dolz, Ramon
%A Reed, Chris
%Y Ajjour, Yamen
%Y Bar-Haim, Roy
%Y El Baff, Roxanne
%Y Liu, Zhexiong
%Y Skitalinskaya, Gabriella
%S Proceedings of the 11th Workshop on Argument Mining (ArgMining 2024)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F gemechu-etal-2024-aries
%X Measuring advances in argument mining is one of the main challenges in the area. Different theories of argument, heterogeneous annotations, and a varied set of argumentation domains make it difficult to contextualise and understand the results reported in different work from a general perspective. In this paper, we present ARIES, a general benchmark for Argument Relation Identification aimed at providing with a standard evaluation for argument mining research. ARIES covers the three different language modelling approaches: sequence and token modelling, and sequence-to-sequence-to-sequence alignment, together with the three main Transformer-based model architectures: encoder-only, decoder-only, and encoder-decoder. Furthermore, the benchmark consists of eight different argument mining datasets, covering the most common argumentation domains, and standardised with the same annotation structures. This paper provides a first comprehensive and comparative set of results in argument mining across a broad range of configurations to compare with, both advancing the state-of-the-art, and establishing a standard way to measure future advances in the area. Across varied task setups and architectures, our experiments reveal consistent challenges in cross-dataset evaluation, with notably poor results. Given the models’ struggle to acquire transferable skills, the task remains challenging, opening avenues for future research.
%R 10.18653/v1/2024.argmining-1.1
%U https://aclanthology.org/2024.argmining-1.1
%U https://doi.org/10.18653/v1/2024.argmining-1.1
%P 1-14

Markdown (Informal)

[ARIES: A General Benchmark for Argument Relation Identification](https://aclanthology.org/2024.argmining-1.1) (Gemechu et al., ArgMining 2024)

• ARIES: A General Benchmark for Argument Relation Identification (Gemechu et al., ArgMining 2024)

ACL

• Debela Gemechu, Ramon Ruiz-Dolz, and Chris Reed. 2024. ARIES: A General Benchmark for Argument Relation Identification. In Proceedings of the 11th Workshop on Argument Mining (ArgMining 2024), pages 1–14, Bangkok, Thailand. Association for Computational Linguistics.