Pretrained language model transfer on neural named entity recognition in Indonesian conversational texts
Table 7: Experiment results on the ST dev data. The original models and the ablation models are trained on ST training data with varying number of training instances. Bold and underline indicates the highest and the second highest test F1 score, respectively.
['Group', 'Model name', 'Unlabeled data for pretrained LM', 'Source domain data', 'Target domain dev F1 score 1%', 'Target domain dev F1 score 5%', 'Target domain dev F1 score 10%', 'Target domain dev F1 score 25%', 'Target domain dev F1 score 50%', 'Target domain dev F1 score 75%', 'Target domain dev F1 score 100%']
An ablation study is conducted for both the supervised and unsupervised fine-tuning. We perform three ablations resulting in four models: two for the supervised fine-tuning group and one for the unsupervised fine-tuning. We carry out the ablations on the development set.