@inproceedings{weinzierl-harabagiu-2020-hltri,
title = "{HLTRI} at {W}-{NUT} 2020 Shared Task-3: {COVID}-19 Event Extraction from {T}witter Using Multi-Task Hopfield Pooling",
author = "Weinzierl, Maxwell and
Harabagiu, Sanda",
editor = "Xu, Wei and
Ritter, Alan and
Baldwin, Tim and
Rahimi, Afshin",
booktitle = "Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.wnut-1.80",
doi = "10.18653/v1/2020.wnut-1.80",
pages = "530--538",
abstract = "Extracting structured knowledge involving self-reported events related to the COVID-19 pandemic from Twitter has the potential to inform surveillance systems that play a critical role in public health. The event extraction challenge presented by the W-NUT 2020 Shared Task 3 focused on the identification of five types of events relevant to the COVID-19 pandemic and their respective set of pre-defined slots encoding demographic, epidemiological, clinical as well as spatial, temporal or subjective knowledge. Our participation in the challenge led to the design of a neural architecture for jointly identifying all Event Slots expressed in a tweet relevant to an event of interest. This architecture uses COVID-Twitter-BERT as the pre-trained language model. In addition, to learn text span embeddings for each Event Slot, we relied on a special case of Hopfield Networks, namely Hopfield pooling. The results of the shared task evaluation indicate that our system performs best when it is trained on a larger dataset, while it remains competitive when training on smaller datasets.",
}
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<abstract>Extracting structured knowledge involving self-reported events related to the COVID-19 pandemic from Twitter has the potential to inform surveillance systems that play a critical role in public health. The event extraction challenge presented by the W-NUT 2020 Shared Task 3 focused on the identification of five types of events relevant to the COVID-19 pandemic and their respective set of pre-defined slots encoding demographic, epidemiological, clinical as well as spatial, temporal or subjective knowledge. Our participation in the challenge led to the design of a neural architecture for jointly identifying all Event Slots expressed in a tweet relevant to an event of interest. This architecture uses COVID-Twitter-BERT as the pre-trained language model. In addition, to learn text span embeddings for each Event Slot, we relied on a special case of Hopfield Networks, namely Hopfield pooling. The results of the shared task evaluation indicate that our system performs best when it is trained on a larger dataset, while it remains competitive when training on smaller datasets.</abstract>
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%0 Conference Proceedings
%T HLTRI at W-NUT 2020 Shared Task-3: COVID-19 Event Extraction from Twitter Using Multi-Task Hopfield Pooling
%A Weinzierl, Maxwell
%A Harabagiu, Sanda
%Y Xu, Wei
%Y Ritter, Alan
%Y Baldwin, Tim
%Y Rahimi, Afshin
%S Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F weinzierl-harabagiu-2020-hltri
%X Extracting structured knowledge involving self-reported events related to the COVID-19 pandemic from Twitter has the potential to inform surveillance systems that play a critical role in public health. The event extraction challenge presented by the W-NUT 2020 Shared Task 3 focused on the identification of five types of events relevant to the COVID-19 pandemic and their respective set of pre-defined slots encoding demographic, epidemiological, clinical as well as spatial, temporal or subjective knowledge. Our participation in the challenge led to the design of a neural architecture for jointly identifying all Event Slots expressed in a tweet relevant to an event of interest. This architecture uses COVID-Twitter-BERT as the pre-trained language model. In addition, to learn text span embeddings for each Event Slot, we relied on a special case of Hopfield Networks, namely Hopfield pooling. The results of the shared task evaluation indicate that our system performs best when it is trained on a larger dataset, while it remains competitive when training on smaller datasets.
%R 10.18653/v1/2020.wnut-1.80
%U https://aclanthology.org/2020.wnut-1.80
%U https://doi.org/10.18653/v1/2020.wnut-1.80
%P 530-538
Markdown (Informal)
[HLTRI at W-NUT 2020 Shared Task-3: COVID-19 Event Extraction from Twitter Using Multi-Task Hopfield Pooling](https://aclanthology.org/2020.wnut-1.80) (Weinzierl & Harabagiu, WNUT 2020)
ACL