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    <abstract>The competition of extracting COVID-19 events from Twitter is to develop systems that can automatically extract related events from tweets. The built system should identify different pre-defined slots for each event, in order to answer important questions (e.g., Who is tested positive? What is the age of the person? Where is he/she?). To tackle these challenges, we propose the Joint Event Multi-task Learning (JOELIN) model. Through a unified global learning framework, we make use of all the training data across different events to learn and fine-tune the language model. Moreover, we implement a type-aware post-processing procedure using named entity recognition (NER) to further filter the predictions. JOELIN outperforms the BERT baseline by 17.2% in micro F1.</abstract>
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%0 Conference Proceedings
%T TEST_POSITIVE at W-NUT 2020 Shared Task-3: Cross-task modeling
%A Chen, Chacha
%A Huang, Chieh-Yang
%A Hou, Yaqi
%A Shi, Yang
%A Dai, Enyan
%A Wang, Jiaqi
%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 chen-etal-2020-test
%X The competition of extracting COVID-19 events from Twitter is to develop systems that can automatically extract related events from tweets. The built system should identify different pre-defined slots for each event, in order to answer important questions (e.g., Who is tested positive? What is the age of the person? Where is he/she?). To tackle these challenges, we propose the Joint Event Multi-task Learning (JOELIN) model. Through a unified global learning framework, we make use of all the training data across different events to learn and fine-tune the language model. Moreover, we implement a type-aware post-processing procedure using named entity recognition (NER) to further filter the predictions. JOELIN outperforms the BERT baseline by 17.2% in micro F1.
%R 10.18653/v1/2020.wnut-1.76
%U https://aclanthology.org/2020.wnut-1.76
%U https://doi.org/10.18653/v1/2020.wnut-1.76
%P 499-504

Markdown (Informal)

[TEST_POSITIVE at W-NUT 2020 Shared Task-3: Cross-task modeling](https://aclanthology.org/2020.wnut-1.76) (Chen et al., WNUT 2020)

• TEST_POSITIVE at W-NUT 2020 Shared Task-3: Cross-task modeling (Chen et al., WNUT 2020)

ACL

• Chacha Chen, Chieh-Yang Huang, Yaqi Hou, Yang Shi, Enyan Dai, and Jiaqi Wang. 2020. TEST_POSITIVE at W-NUT 2020 Shared Task-3: Cross-task modeling. In Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020), pages 499–504, Online. Association for Computational Linguistics.