@inproceedings{liu-etal-2019-columbia,
title = "{C}olumbia at {S}em{E}val-2019 Task 7: Multi-task Learning for Stance Classification and Rumour Verification",
author = "Liu, Zhuoran and
Goel, Shivali and
Yelahanka Raghuprasad, Mukund and
Muresan, Smaranda",
editor = "May, Jonathan and
Shutova, Ekaterina and
Herbelot, Aurelie and
Zhu, Xiaodan and
Apidianaki, Marianna and
Mohammad, Saif M.",
booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S19-2194",
doi = "10.18653/v1/S19-2194",
pages = "1110--1114",
abstract = "The paper presents Columbia team{'}s participation in the SemEval 2019 Shared Task 7: RumourEval 2019. Detecting rumour on social networks has been a focus of research in recent years. Previous work suffered from data sparsity, which potentially limited the application of more sophisticated neural architecture to this task. We mitigate this problem by proposing a multi-task learning approach together with language model fine-tuning. Our attention-based model allows different tasks to leverage different level of information. Our system ranked 6th overall with an F1-score of 36.25 on stance classification and F1 of 22.44 on rumour verification.",
}
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<abstract>The paper presents Columbia team’s participation in the SemEval 2019 Shared Task 7: RumourEval 2019. Detecting rumour on social networks has been a focus of research in recent years. Previous work suffered from data sparsity, which potentially limited the application of more sophisticated neural architecture to this task. We mitigate this problem by proposing a multi-task learning approach together with language model fine-tuning. Our attention-based model allows different tasks to leverage different level of information. Our system ranked 6th overall with an F1-score of 36.25 on stance classification and F1 of 22.44 on rumour verification.</abstract>
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%0 Conference Proceedings
%T Columbia at SemEval-2019 Task 7: Multi-task Learning for Stance Classification and Rumour Verification
%A Liu, Zhuoran
%A Goel, Shivali
%A Yelahanka Raghuprasad, Mukund
%A Muresan, Smaranda
%Y May, Jonathan
%Y Shutova, Ekaterina
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%S Proceedings of the 13th International Workshop on Semantic Evaluation
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota, USA
%F liu-etal-2019-columbia
%X The paper presents Columbia team’s participation in the SemEval 2019 Shared Task 7: RumourEval 2019. Detecting rumour on social networks has been a focus of research in recent years. Previous work suffered from data sparsity, which potentially limited the application of more sophisticated neural architecture to this task. We mitigate this problem by proposing a multi-task learning approach together with language model fine-tuning. Our attention-based model allows different tasks to leverage different level of information. Our system ranked 6th overall with an F1-score of 36.25 on stance classification and F1 of 22.44 on rumour verification.
%R 10.18653/v1/S19-2194
%U https://aclanthology.org/S19-2194
%U https://doi.org/10.18653/v1/S19-2194
%P 1110-1114
Markdown (Informal)
[Columbia at SemEval-2019 Task 7: Multi-task Learning for Stance Classification and Rumour Verification](https://aclanthology.org/S19-2194) (Liu et al., SemEval 2019)
ACL