@inproceedings{falk-lapesa-2023-bridging,
title = "Bridging Argument Quality and Deliberative Quality Annotations with Adapters",
author = "Falk, Neele and
Lapesa, Gabriella",
editor = "Vlachos, Andreas and
Augenstein, Isabelle",
booktitle = "Findings of the Association for Computational Linguistics: EACL 2023",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-eacl.187",
doi = "10.18653/v1/2023.findings-eacl.187",
pages = "2469--2488",
abstract = "Assessing the quality of an argument is a complex, highly subjective task, influenced by heterogeneous factors (e.g., prior beliefs of the annotators, topic, domain, and application), and crucial for its impact in downstream tasks (e.g., argument retrieval or generation). Both the Argument Mining and the Social Science community have devoted plenty of attention to it, resulting in a wide variety of argument quality dimensions and a large number of annotated resources. This work aims at a better understanding of how the different aspects of argument quality relate to each other from a practical point of view. We employ adapter-fusion (Pfeiffer et al., 2021) as a multi-task learning framework which a) can improve the prediction of individual quality dimensions by injecting knowledge about related dimensions b) is efficient and modular and c) can serve as an analysis tool to investigate relations between different dimensions. We conduct experiments on 6 datasets and 20 quality dimensions. We find that the majority of the dimensions can be learned as a weighted combination of other quality aspects, and that for 8 dimensions adapter fusion improves quality prediction. Last, we show the benefits of this approach by improving the performance in an extrinsic, out-of-domain task: prediction of moderator interventions in a deliberative forum.",
}
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<abstract>Assessing the quality of an argument is a complex, highly subjective task, influenced by heterogeneous factors (e.g., prior beliefs of the annotators, topic, domain, and application), and crucial for its impact in downstream tasks (e.g., argument retrieval or generation). Both the Argument Mining and the Social Science community have devoted plenty of attention to it, resulting in a wide variety of argument quality dimensions and a large number of annotated resources. This work aims at a better understanding of how the different aspects of argument quality relate to each other from a practical point of view. We employ adapter-fusion (Pfeiffer et al., 2021) as a multi-task learning framework which a) can improve the prediction of individual quality dimensions by injecting knowledge about related dimensions b) is efficient and modular and c) can serve as an analysis tool to investigate relations between different dimensions. We conduct experiments on 6 datasets and 20 quality dimensions. We find that the majority of the dimensions can be learned as a weighted combination of other quality aspects, and that for 8 dimensions adapter fusion improves quality prediction. Last, we show the benefits of this approach by improving the performance in an extrinsic, out-of-domain task: prediction of moderator interventions in a deliberative forum.</abstract>
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%0 Conference Proceedings
%T Bridging Argument Quality and Deliberative Quality Annotations with Adapters
%A Falk, Neele
%A Lapesa, Gabriella
%Y Vlachos, Andreas
%Y Augenstein, Isabelle
%S Findings of the Association for Computational Linguistics: EACL 2023
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F falk-lapesa-2023-bridging
%X Assessing the quality of an argument is a complex, highly subjective task, influenced by heterogeneous factors (e.g., prior beliefs of the annotators, topic, domain, and application), and crucial for its impact in downstream tasks (e.g., argument retrieval or generation). Both the Argument Mining and the Social Science community have devoted plenty of attention to it, resulting in a wide variety of argument quality dimensions and a large number of annotated resources. This work aims at a better understanding of how the different aspects of argument quality relate to each other from a practical point of view. We employ adapter-fusion (Pfeiffer et al., 2021) as a multi-task learning framework which a) can improve the prediction of individual quality dimensions by injecting knowledge about related dimensions b) is efficient and modular and c) can serve as an analysis tool to investigate relations between different dimensions. We conduct experiments on 6 datasets and 20 quality dimensions. We find that the majority of the dimensions can be learned as a weighted combination of other quality aspects, and that for 8 dimensions adapter fusion improves quality prediction. Last, we show the benefits of this approach by improving the performance in an extrinsic, out-of-domain task: prediction of moderator interventions in a deliberative forum.
%R 10.18653/v1/2023.findings-eacl.187
%U https://aclanthology.org/2023.findings-eacl.187
%U https://doi.org/10.18653/v1/2023.findings-eacl.187
%P 2469-2488
Markdown (Informal)
[Bridging Argument Quality and Deliberative Quality Annotations with Adapters](https://aclanthology.org/2023.findings-eacl.187) (Falk & Lapesa, Findings 2023)
ACL