@inproceedings{conforti-etal-2021-synthetic,
title = "Synthetic Examples Improve Cross-Target Generalization: A Study on Stance Detection on a {T}witter corpus.",
author = "Conforti, Costanza and
Berndt, Jakob and
Pilehvar, Mohammad Taher and
Giannitsarou, Chryssi and
Toxvaerd, Flavio and
Collier, Nigel",
editor = "De Clercq, Orphee and
Balahur, Alexandra and
Sedoc, Joao and
Barriere, Valentin and
Tafreshi, Shabnam and
Buechel, Sven and
Hoste, Veronique",
booktitle = "Proceedings of the Eleventh Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
month = apr,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.wassa-1.19",
pages = "181--187",
abstract = "Cross-target generalization is a known problem in stance detection (SD), where systems tend to perform poorly when exposed to targets unseen during training. Given that data annotation is expensive and time-consuming, finding ways to leverage abundant unlabeled in-domain data can offer great benefits. In this paper, we apply a weakly supervised framework to enhance cross-target generalization through synthetically annotated data. We focus on Twitter SD and show experimentally that integrating synthetic data is helpful for cross-target generalization, leading to significant improvements in performance, with gains in F1 scores ranging from +3.4 to +5.1.",
}
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%0 Conference Proceedings
%T Synthetic Examples Improve Cross-Target Generalization: A Study on Stance Detection on a Twitter corpus.
%A Conforti, Costanza
%A Berndt, Jakob
%A Pilehvar, Mohammad Taher
%A Giannitsarou, Chryssi
%A Toxvaerd, Flavio
%A Collier, Nigel
%Y De Clercq, Orphee
%Y Balahur, Alexandra
%Y Sedoc, Joao
%Y Barriere, Valentin
%Y Tafreshi, Shabnam
%Y Buechel, Sven
%Y Hoste, Veronique
%S Proceedings of the Eleventh Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
%D 2021
%8 April
%I Association for Computational Linguistics
%C Online
%F conforti-etal-2021-synthetic
%X Cross-target generalization is a known problem in stance detection (SD), where systems tend to perform poorly when exposed to targets unseen during training. Given that data annotation is expensive and time-consuming, finding ways to leverage abundant unlabeled in-domain data can offer great benefits. In this paper, we apply a weakly supervised framework to enhance cross-target generalization through synthetically annotated data. We focus on Twitter SD and show experimentally that integrating synthetic data is helpful for cross-target generalization, leading to significant improvements in performance, with gains in F1 scores ranging from +3.4 to +5.1.
%U https://aclanthology.org/2021.wassa-1.19
%P 181-187
Markdown (Informal)
[Synthetic Examples Improve Cross-Target Generalization: A Study on Stance Detection on a Twitter corpus.](https://aclanthology.org/2021.wassa-1.19) (Conforti et al., WASSA 2021)
ACL