Neural Multi-Task Learning for Stance Prediction

Wei Fang, Moin Nadeem, Mitra Mohtarami, James Glass


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.
Anthology ID:
D19-6603
Volume:
Proceedings of the Second Workshop on Fact Extraction and VERification (FEVER)
Month:
November
Year:
2019
Address:
Hong Kong, China
Venues:
EMNLP | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
13–19
Language:
URL:
https://www.aclweb.org/anthology/D19-6603
DOI:
10.18653/v1/D19-6603
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PDF:
http://aclanthology.lst.uni-saarland.de/D19-6603.pdf