Evidence Inference 2.0: More Data, Better Models

Jay DeYoung, Eric Lehman, Benjamin Nye, Iain Marshall, Byron C. Wallace


Abstract
How do we most effectively treat a disease or condition? Ideally, we could consult a database of evidence gleaned from clinical trials to answer such questions. Unfortunately, no such database exists; clinical trial results are instead disseminated primarily via lengthy natural language articles. Perusing all such articles would be prohibitively time-consuming for healthcare practitioners; they instead tend to depend on manually compiled systematic reviews of medical literature to inform care. NLP may speed this process up, and eventually facilitate immediate consult of published evidence. The Evidence Inference dataset was recently released to facilitate research toward this end. This task entails inferring the comparative performance of two treatments, with respect to a given outcome, from a particular article (describing a clinical trial) and identifying supporting evidence. For instance: Does this article report that chemotherapy performed better than surgery for five-year survival rates of operable cancers? In this paper, we collect additional annotations to expand the Evidence Inference dataset by 25%, provide stronger baseline models, systematically inspect the errors that these make, and probe dataset quality. We also release an abstract only (as opposed to full-texts) version of the task for rapid model prototyping. The updated corpus, documentation, and code for new baselines and evaluations are available at http://evidence-inference.ebm-nlp.com/.
Anthology ID:
2020.bionlp-1.13
Volume:
Proceedings of the 19th SIGBioMed Workshop on Biomedical Language Processing
Month:
July
Year:
2020
Address:
Online
Venues:
ACL | BioNLP | WS
SIG:
SIGBIOMED
Publisher:
Association for Computational Linguistics
Note:
Pages:
123–132
Language:
URL:
https://www.aclweb.org/anthology/2020.bionlp-1.13
DOI:
10.18653/v1/2020.bionlp-1.13
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.bionlp-1.13.pdf