Cross-Lingual Transfer for Distantly Supervised and Low-resources Indonesian NER
Table 4: Experiment on silver standard annotation of Indonesian NER evaluated on Gold test set [1] in large distantly supervised NER scenario. Bold F1 scores are best result per scenarios (Baseline, Supervised Cross-lingual Transfer, Cross-lingual using ELMo from EN, Mono-lingual ELMo and Unsupervised-Supervised Cross-lingual Transfer). * is the best model on a dataset (DEE, MDEE, or +Gazz) on all model scenarios
['Model', 'Prec', 'Rec', 'F1']
Alfina et al We also confirmed that, there are many cases of false negative in orthographically similar LOCATION alias such as ”Pacific” to ”Pasifik” in Indonesian Wikipedia. using pre-trained weights from state-of-the-arts NER model that uses Bidirectional language model. Our motivation to propose this method is because we observed that there are only marginal improvement using monolingual Indonesia LM of 82M Tokens from Wikipedia compared to using English LM trained on 1B Tokens on applying ELMo to Distantly Supervised NER dataset. This might be attributed due to large difference of publicly available unlabeled corpus size, such as 82M in benchmark or 2.9B English Wikipedia available to train.