From English to Code-Switching: Transfer Learningwith Strong Morphological Clues
Table 4: The F1 scores on POS tagging for the Hindi-English dataset. CS knowledge means that the CS-ELMo architecture (see Figure 2A) has been adapted to code-switching by using the LID task.
['[BOLD] POS System', '[BOLD] Dev F1', '[BOLD] Test F1']
When we compare our CS-ELMO + BLSTM + CRF model without CS adaptation (Exp 4.1) against the baseline (ELMo + BLSTM + CRF), the performance remains similar. This suggests that our enhanced n-gram mechanism can be added to ELMo without impacting the performance even if the model has not been adapted to CS. Slightly better performance is achieved when the CS-ELMo has been adapted to code-switching, and only the BLSTM and CRF layers are retrained (Exp 4.2). This result shows the convenience of our model since small improvements can be achieved faster by leveraging the already-learned CS knowledge while avoiding to retrain the entire model. Nevertheless, the best performance is achieved by the adapted CS-ELMO + BLSTM + CRF when retraining the entire model (Exp 4.3). Our results are better than the baselines and the previous state of the art.