@inproceedings{fang-etal-2019-neural,
title = "Neural Multi-Task Learning for Stance Prediction",
author = "Fang, Wei and
Nadeem, Moin and
Mohtarami, Mitra and
Glass, James",
editor = "Thorne, James and
Vlachos, Andreas and
Cocarascu, Oana and
Christodoulopoulos, Christos and
Mittal, Arpit",
booktitle = "Proceedings of the Second Workshop on Fact Extraction and VERification (FEVER)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-6603",
doi = "10.18653/v1/D19-6603",
pages = "13--19",
abstract = "We present a multi-task learning model that leverages large amount of textual information from existing datasets to improve stance prediction. In particular, we utilize multiple NLP tasks under both unsupervised and supervised settings for the target stance prediction task. Our model obtains state-of-the-art performance on a public benchmark dataset, Fake News Challenge, outperforming current approaches by a wide margin.",
}
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<abstract>We present a multi-task learning model that leverages large amount of textual information from existing datasets to improve stance prediction. In particular, we utilize multiple NLP tasks under both unsupervised and supervised settings for the target stance prediction task. Our model obtains state-of-the-art performance on a public benchmark dataset, Fake News Challenge, outperforming current approaches by a wide margin.</abstract>
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%0 Conference Proceedings
%T Neural Multi-Task Learning for Stance Prediction
%A Fang, Wei
%A Nadeem, Moin
%A Mohtarami, Mitra
%A Glass, James
%Y Thorne, James
%Y Vlachos, Andreas
%Y Cocarascu, Oana
%Y Christodoulopoulos, Christos
%Y Mittal, Arpit
%S Proceedings of the Second Workshop on Fact Extraction and VERification (FEVER)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F fang-etal-2019-neural
%X We present a multi-task learning model that leverages large amount of textual information from existing datasets to improve stance prediction. In particular, we utilize multiple NLP tasks under both unsupervised and supervised settings for the target stance prediction task. Our model obtains state-of-the-art performance on a public benchmark dataset, Fake News Challenge, outperforming current approaches by a wide margin.
%R 10.18653/v1/D19-6603
%U https://aclanthology.org/D19-6603
%U https://doi.org/10.18653/v1/D19-6603
%P 13-19
Markdown (Informal)
[Neural Multi-Task Learning for Stance Prediction](https://aclanthology.org/D19-6603) (Fang et al., 2019)
ACL
- Wei Fang, Moin Nadeem, Mitra Mohtarami, and James Glass. 2019. Neural Multi-Task Learning for Stance Prediction. In Proceedings of the Second Workshop on Fact Extraction and VERification (FEVER), pages 13–19, Hong Kong, China. Association for Computational Linguistics.