TEST_POSITIVE at W-NUT 2020 Shared Task-3: Joint Event Multi-task Learning for Slot Filling in Noisy Text

• URL: http://arxiv.org/abs/2009.14262v1
• Date: Tue, 29 Sep 2020 19:08:45 GMT
• Title: TEST_POSITIVE at W-NUT 2020 Shared Task-3: Joint Event Multi-task Learning for Slot Filling in Noisy Text
• Authors: Chacha Chen, Chieh-Yang Huang, Yaqi Hou, Yang Shi, Enyan Dai, Jiaqi Wang
• Abstract summary: We propose the Joint Event Multi-task Learning (JOELIN) model for extracting COVID-19 events from Twitter. 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. We implement a type-aware post-processing procedure using named entity recognition (NER) to further filter the predictions.
• Score: 26.270447944466557
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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