UBC-NLP at SemEval-2019 Task 4: Hyperpartisan News Detection With Attention-Based Bi-LSTMs

Chiyu Zhang; Arun Rajendran; Muhammad Abdul-Mageed

doi:10.18653/v1/S19-2188

UBC-NLP at SemEval-2019 Task 4: Hyperpartisan News Detection With Attention-Based Bi-LSTMs

Chiyu Zhang, Arun Rajendran, Muhammad Abdul-Mageed

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Abstract

We present our deep learning models submitted to the SemEval-2019 Task 4 competition focused at Hyperpartisan News Detection. We acquire best results with a Bi-LSTM network equipped with a self-attention mechanism. Among 33 participating teams, our submitted system ranks top 7 (65.3% accuracy) on the ‘labels-by-publisher’ sub-task and top 24 out of 44 teams (68.3% accuracy) on the ‘labels-by-article’ sub-task (65.3% accuracy). We also report a model that scores higher than the 8th ranking system (78.5% accuracy) on the ‘labels-by-article’ sub-task.

Anthology ID:: S19-2188
Volume:: Proceedings of the 13th International Workshop on Semantic Evaluation
Month:: June
Year:: 2019
Address:: Minneapolis, Minnesota, USA
Editors:: Jonathan May, Ekaterina Shutova, Aurelie Herbelot, Xiaodan Zhu, Marianna Apidianaki, Saif M. Mohammad
Venue:: SemEval
SIG:: SIGLEX
Publisher:: Association for Computational Linguistics
Note:
Pages:: 1072–1077
Language:
URL:: https://aclanthology.org/S19-2188/
DOI:: 10.18653/v1/S19-2188
Bibkey:
Cite (ACL):: Chiyu Zhang, Arun Rajendran, and Muhammad Abdul-Mageed. 2019. UBC-NLP at SemEval-2019 Task 4: Hyperpartisan News Detection With Attention-Based Bi-LSTMs. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 1072–1077, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
Cite (Informal):: UBC-NLP at SemEval-2019 Task 4: Hyperpartisan News Detection With Attention-Based Bi-LSTMs (Zhang et al., SemEval 2019)
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PDF:: https://aclanthology.org/S19-2188.pdf

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@inproceedings{zhang-etal-2019-ubc,
    title = "{UBC}-{NLP} at {S}em{E}val-2019 Task 4: Hyperpartisan News Detection With Attention-Based {B}i-{LSTM}s",
    author = "Zhang, Chiyu  and
      Rajendran, Arun  and
      Abdul-Mageed, Muhammad",
    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/S19-2188/",
    doi = "10.18653/v1/S19-2188",
    pages = "1072--1077",
    abstract = "We present our deep learning models submitted to the SemEval-2019 Task 4 competition focused at Hyperpartisan News Detection. We acquire best results with a Bi-LSTM network equipped with a self-attention mechanism. Among 33 participating teams, our submitted system ranks top 7 (65.3{\%} accuracy) on the `labels-by-publisher' sub-task and top 24 out of 44 teams (68.3{\%} accuracy) on the `labels-by-article' sub-task (65.3{\%} accuracy). We also report a model that scores higher than the 8th ranking system (78.5{\%} accuracy) on the `labels-by-article' sub-task."
}

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%0 Conference Proceedings
%T UBC-NLP at SemEval-2019 Task 4: Hyperpartisan News Detection With Attention-Based Bi-LSTMs
%A Zhang, Chiyu
%A Rajendran, Arun
%A Abdul-Mageed, Muhammad
%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 zhang-etal-2019-ubc
%X We present our deep learning models submitted to the SemEval-2019 Task 4 competition focused at Hyperpartisan News Detection. We acquire best results with a Bi-LSTM network equipped with a self-attention mechanism. Among 33 participating teams, our submitted system ranks top 7 (65.3% accuracy) on the ‘labels-by-publisher’ sub-task and top 24 out of 44 teams (68.3% accuracy) on the ‘labels-by-article’ sub-task (65.3% accuracy). We also report a model that scores higher than the 8th ranking system (78.5% accuracy) on the ‘labels-by-article’ sub-task.
%R 10.18653/v1/S19-2188
%U https://aclanthology.org/S19-2188/
%U https://doi.org/10.18653/v1/S19-2188
%P 1072-1077

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Markdown (Informal)

[UBC-NLP at SemEval-2019 Task 4: Hyperpartisan News Detection With Attention-Based Bi-LSTMs](https://aclanthology.org/S19-2188/) (Zhang et al., SemEval 2019)

UBC-NLP at SemEval-2019 Task 4: Hyperpartisan News Detection With Attention-Based Bi-LSTMs (Zhang et al., SemEval 2019)

ACL

Chiyu Zhang, Arun Rajendran, and Muhammad Abdul-Mageed. 2019. UBC-NLP at SemEval-2019 Task 4: Hyperpartisan News Detection With Attention-Based Bi-LSTMs. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 1072–1077, Minneapolis, Minnesota, USA. Association for Computational Linguistics.