ComplexDataLab at W-NUT 2020 Task 2: Detecting Informative COVID-19 Tweets by Attending over Linked Documents

Kellin Pelrine, Jacob Danovitch, Albert Orozco Camacho, Reihaneh Rabbany


Abstract
Given the global scale of COVID-19 and the flood of social media content related to it, how can we find informative discussions? We present Gapformer, which effectively classifies content as informative or not. It reformulates the problem as graph classification, drawing on not only the tweet but connected webpages and entities. We leverage a pre-trained language model as well as the connections between nodes to learn a pooled representation for each document network. We show it outperforms several competitive baselines and present ablation studies supporting the benefit of the linked information. Code is available on Github.
Anthology ID:
2020.wnut-1.63
Volume:
Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)
Month:
November
Year:
2020
Address:
Online
Editors:
Wei Xu, Alan Ritter, Tim Baldwin, Afshin Rahimi
Venue:
WNUT
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
434–439
Language:
URL:
https://aclanthology.org/2020.wnut-1.63
DOI:
10.18653/v1/2020.wnut-1.63
Bibkey:
Cite (ACL):
Kellin Pelrine, Jacob Danovitch, Albert Orozco Camacho, and Reihaneh Rabbany. 2020. ComplexDataLab at W-NUT 2020 Task 2: Detecting Informative COVID-19 Tweets by Attending over Linked Documents. In Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020), pages 434–439, Online. Association for Computational Linguistics.
Cite (Informal):
ComplexDataLab at W-NUT 2020 Task 2: Detecting Informative COVID-19 Tweets by Attending over Linked Documents (Pelrine et al., WNUT 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.wnut-1.63.pdf
Data
WNUT-2020 Task 2