Approaching SMM4H with Merged Models and Multi-task Learning

Tilia Ellendorff, Lenz Furrer, Nicola Colic, Noëmi Aepli, Fabio Rinaldi


Abstract
We describe our submissions to the 4th edition of the Social Media Mining for Health Applications (SMM4H) shared task. Our team (UZH) participated in two sub-tasks: Automatic classifications of adverse effects mentions in tweets (Task 1) and Generalizable identification of personal health experience mentions (Task 4). For our submissions, we exploited ensembles based on a pre-trained language representation with a neural transformer architecture (BERT) (Tasks 1 and 4) and a CNN-BiLSTM(-CRF) network within a multi-task learning scenario (Task 1). These systems are placed on top of a carefully crafted pipeline of domain-specific preprocessing steps.
Anthology ID:
W19-3208
Volume:
Proceedings of the Fourth Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task
Month:
August
Year:
2019
Address:
Florence, Italy
Venues:
ACL | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
58–61
Language:
URL:
https://www.aclweb.org/anthology/W19-3208
DOI:
10.18653/v1/W19-3208
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PDF:
http://aclanthology.lst.uni-saarland.de/W19-3208.pdf