TEST_POSITIVE at W-NUT 2020 Shared Task-3: Joint Event Multi-task Learning for Slot Filling in Noisy Text
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Extracting COVID-19 Events from TwitterLanguage ModelingLanguage ModellingMulti-Task Learningnamed-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)NERslot-fillingSlot FillingMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
TEST_POSITIVE at W-NUT 2020 Shared Task-3: Cross-task modeling
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 …
Extracting COVID-19 Events from TwitterLanguage ModelingLanguage ModellingMulti-Task Learning+4imec-ETRO-VUB at W-NUT 2020 Shared Task-3: A multilabel BERT-based system for predicting COVID-19 events
In this paper, we present our system designed to address the W-NUT 2020 shared task for COVID-19 Event Extraction from Twitter. To mitigate the noisy nature of the Twitter stream, our system makes use of the COVID-Twitte…
Event ExtractionLanguage ModelingLanguage ModellingCOVID-19 event extraction from Twitter via extractive question answering with continuous prompts
As COVID-19 ravages the world, social media analytics could augment traditional surveys in assessing how the pandemic evolves and capturing consumer chatter that could help healthcare agencies in addressing it. This typi…
BenchmarkingEvent ExtractionExtractive Question-AnsweringQuestion Answering1Cademy @ Causal News Corpus 2022: Leveraging Self-Training in Causality Classification of Socio-Political Event Data
This paper details our participation in the Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE) workshop @ EMNLP 2022, where we take part in Subtask 1 of Shared Task 3. We appro…
DelistBench: Evaluating Search-Enabled LLMs for Auditable Corporate-Event Database Completion
Financial institutions need an independent way to detect missing, stale, and misclassified corporate-event records in vendor databases. We introduce Search-to-Record, a database-assurance task in which search-enabled lar…