EMNLP 2024main1 citations
Let’s discuss! Quality Dimensions and Annotated Datasets for Computational Argument Quality Assessment
Rositsa V Ivanova, Thomas Huber, Christina Niklaus
Abstract
Research in the computational assessment of Argumentation Quality has gained popularity over the last ten years. Various quality dimensions have been explored through the creation of domain-specific datasets and assessment methods. We survey the related literature (211 publications and 32 datasets), while addressing potential overlaps and blurry boundaries to related domains. This paper provides a representative overview of the state of the art in Computational Argument Quality Assessment with a focus on quality dimensions and annotated datasets. The aim of the survey is to identify research gaps and to aid future discussions and work in the domain.
BibTeX
@inproceedings{ivanova-etal-2024-lets,
title = "Let`s discuss! Quality Dimensions and Annotated Datasets for Computational Argument Quality Assessment",
author = "Ivanova, Rositsa V and
Huber, Thomas and
Niklaus, Christina",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-main.1155/",
doi = "10.18653/v1/2024.emnlp-main.1155",
pages = "20749--20779"
}