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
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 58–61
- Language:
- URL:
- https://aclanthology.org/W19-3208
- DOI:
- 10.18653/v1/W19-3208
- Cite (ACL):
- Tilia Ellendorff, Lenz Furrer, Nicola Colic, Noëmi Aepli, and Fabio Rinaldi. 2019. Approaching SMM4H with Merged Models and Multi-task Learning. In Proceedings of the Fourth Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task, pages 58–61, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- Approaching SMM4H with Merged Models and Multi-task Learning (Ellendorff et al., ACL 2019)
- PDF:
- https://preview.aclanthology.org/starsem-semeval-split/W19-3208.pdf