@inproceedings{yasuda-etal-2020-nhk,
title = "{NHK}{\_}{STRL} at {WNUT}-2020 Task 2: {GAT}s with Syntactic Dependencies as Edges and {CTC}-based Loss for Text Classification",
author = "Yasuda, Yuki and
Ishiwatari, Taichi and
Miyazaki, Taro and
Goto, Jun",
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-1.43",
doi = "10.18653/v1/2020.wnut-1.43",
pages = "324--330",
abstract = "The outbreak of COVID-19 has greatly impacted our daily lives. In these circumstances, it is important to grasp the latest information to avoid causing too much fear and panic. To help grasp new information, extracting information from social networking sites is one of the effective ways. In this paper, we describe a method to identify whether a tweet related to COVID-19 is informative or not, which can help to grasp new information. The key features of our method are its use of graph attention networks to encode syntactic dependencies and word positions in the sentence, and a loss function based on connectionist temporal classification (CTC) that can learn a label for each token without reference data for each token. Experimental results show that the proposed method achieved an F1 score of 0.9175, out- performing baseline methods.",
}
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<abstract>The outbreak of COVID-19 has greatly impacted our daily lives. In these circumstances, it is important to grasp the latest information to avoid causing too much fear and panic. To help grasp new information, extracting information from social networking sites is one of the effective ways. In this paper, we describe a method to identify whether a tweet related to COVID-19 is informative or not, which can help to grasp new information. The key features of our method are its use of graph attention networks to encode syntactic dependencies and word positions in the sentence, and a loss function based on connectionist temporal classification (CTC) that can learn a label for each token without reference data for each token. Experimental results show that the proposed method achieved an F1 score of 0.9175, out- performing baseline methods.</abstract>
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%0 Conference Proceedings
%T NHK_STRL at WNUT-2020 Task 2: GATs with Syntactic Dependencies as Edges and CTC-based Loss for Text Classification
%A Yasuda, Yuki
%A Ishiwatari, Taichi
%A Miyazaki, Taro
%A Goto, Jun
%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 yasuda-etal-2020-nhk
%X The outbreak of COVID-19 has greatly impacted our daily lives. In these circumstances, it is important to grasp the latest information to avoid causing too much fear and panic. To help grasp new information, extracting information from social networking sites is one of the effective ways. In this paper, we describe a method to identify whether a tweet related to COVID-19 is informative or not, which can help to grasp new information. The key features of our method are its use of graph attention networks to encode syntactic dependencies and word positions in the sentence, and a loss function based on connectionist temporal classification (CTC) that can learn a label for each token without reference data for each token. Experimental results show that the proposed method achieved an F1 score of 0.9175, out- performing baseline methods.
%R 10.18653/v1/2020.wnut-1.43
%U https://aclanthology.org/2020.wnut-1.43
%U https://doi.org/10.18653/v1/2020.wnut-1.43
%P 324-330
Markdown (Informal)
[NHK_STRL at WNUT-2020 Task 2: GATs with Syntactic Dependencies as Edges and CTC-based Loss for Text Classification](https://aclanthology.org/2020.wnut-1.43) (Yasuda et al., WNUT 2020)
ACL