Pretrained language model transfer on neural named entity recognition in Indonesian conversational texts
Table 4: Accuracy of the baseline model and the baselines with the pretrained embeddings (ELMo and Flair trained on Idwiki and KT data) trained and evaluated on TO data.
['[BOLD] Models', '[BOLD] F1 Dev', '[BOLD] F1 Test']
ELMo yields small absolute improvement of dev F1 score when trained on both Idwiki and KT than the baseline. Flair, although obtains roughly the same dev F1 score when trained on Idwiki, does not perform well when trained on KT. This result might be attributed to the fact that KT contains far fewer sentences compared to IdWiki (around 9 times fewer). Therefore, we do not proceed with Flair for the next experiment with our two approaches.