BiTeM at WNUT 2020 Shared Task-1: Named Entity Recognition over Wet Lab Protocols using an Ensemble of Contextual Language Models
Julien Knafou, Nona Naderi, Jenny Copara, Douglas Teodoro, Patrick Ruch
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 F 1 -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.