BiTeM at WNUT 2020 Shared Task-1: Named Entity Recognition over Wet Lab Protocols using an Ensemble of Contextual Language Models

TitleBiTeM at WNUT 2020 Shared Task-1: Named Entity Recognition over Wet Lab Protocols using an Ensemble of Contextual Language Models
Publication TypeConference Paper
Year of Publication2020
AuthorsKnafou, J, Naderi, N, Copara, J, Teodoro, D, Ruch, P
Conference NameProceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)
PublisherAssociation for Computational Linguistics
Conference LocationOnline
Abstract

Recent improvements in machine-reading technologies attracted much attention to automation problems and their possibilities. In this context, WNUT 2020 introduces a Name Entity Recognition (NER) task based on wet laboratory procedures. In this paper, we present a 3-step method based on deep neural language models that reported the best overall exact match F1-score (77.99%) of the competition. By fine-tuning 10 times, 10 different pretrained language models, this work shows the advantage of having more models in an ensemble based on a majority of votes strategy. On top of that, having 100 different models allowed us to analyse the combinations of ensemble that demonstrated the impact of having multiple pretrained models versus fine-tuning a pretrained model multiple times.

URLhttps://www.aclweb.org/anthology/2020.wnut-1.40