Unsupervised Domain Adaptation of Contextualized Embeddings for Sequence Labeling
Table 4: Named entity segmentation performance on the wnut test set and CoNLL test set A. limsopatham-collier-2016-bidirectional had the winning system at the 2016 wnut shared task. Their results are reprinted from their paper, which did not report performance on the CoNLL dataset.
['System', '[BOLD] wnut domain adaptation data', '[BOLD] wnut Precision', '[BOLD] wnut Recall', '[BOLD] wnut  [BOLD] F1', 'CoNLL F1']
A baseline system using task-tuned BERT (trained on the CoNLL labeled data) achieves an F1 of 57.7 (line 1). This outperforms six of the ten submissions to the wnut shared task, even though these systems are trained on in-domain data. AdaptaBERT yields marginal improvements when domain-adaptive fine-tuning is performed on the wnut training set (line 2); expanding the target domain data with an additional million unlabeled tweets yields a 2.3% improvement over the BERT baseline (line 3). Performance improves considerably when the domain-adaptive fine-tuning is performed on the combined wnut training and test sets (line 4).