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 · 2020
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.