@inproceedings{conforti-etal-2020-stander,
title = "{STANDER}: An Expert-Annotated Dataset for News Stance Detection and Evidence Retrieval",
author = "Conforti, Costanza and
Berndt, Jakob and
Pilehvar, Mohammad Taher and
Giannitsarou, Chryssi and
Toxvaerd, Flavio and
Collier, Nigel",
editor = "Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.findings-emnlp.365",
doi = "10.18653/v1/2020.findings-emnlp.365",
pages = "4086--4101",
abstract = "We present a new challenging news dataset that targets both stance detection (SD) and fine-grained evidence retrieval (ER). With its 3,291 expert-annotated articles, the dataset constitutes a high-quality benchmark for future research in SD and multi-task learning. We provide a detailed description of the corpus collection methodology and carry out an extensive analysis on the sources of disagreement between annotators, observing a correlation between their disagreement and the diffusion of uncertainty around a target in the real world. Our experiments show that the dataset poses a strong challenge to recent state-of-the-art models. Notably, our dataset aligns with an existing Twitter SD dataset: their union thus addresses a key shortcoming of previous works, by providing the first dedicated resource to study multi-genre SD as well as the interplay of signals from social media and news sources in rumour verification.",
}
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%0 Conference Proceedings
%T STANDER: An Expert-Annotated Dataset for News Stance Detection and Evidence Retrieval
%A Conforti, Costanza
%A Berndt, Jakob
%A Pilehvar, Mohammad Taher
%A Giannitsarou, Chryssi
%A Toxvaerd, Flavio
%A Collier, Nigel
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Findings of the Association for Computational Linguistics: EMNLP 2020
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F conforti-etal-2020-stander
%X We present a new challenging news dataset that targets both stance detection (SD) and fine-grained evidence retrieval (ER). With its 3,291 expert-annotated articles, the dataset constitutes a high-quality benchmark for future research in SD and multi-task learning. We provide a detailed description of the corpus collection methodology and carry out an extensive analysis on the sources of disagreement between annotators, observing a correlation between their disagreement and the diffusion of uncertainty around a target in the real world. Our experiments show that the dataset poses a strong challenge to recent state-of-the-art models. Notably, our dataset aligns with an existing Twitter SD dataset: their union thus addresses a key shortcoming of previous works, by providing the first dedicated resource to study multi-genre SD as well as the interplay of signals from social media and news sources in rumour verification.
%R 10.18653/v1/2020.findings-emnlp.365
%U https://aclanthology.org/2020.findings-emnlp.365
%U https://doi.org/10.18653/v1/2020.findings-emnlp.365
%P 4086-4101
Markdown (Informal)
[STANDER: An Expert-Annotated Dataset for News Stance Detection and Evidence Retrieval](https://aclanthology.org/2020.findings-emnlp.365) (Conforti et al., Findings 2020)
ACL