Back Attention Knowledge Transfer for Low-resource Named Entity Recognition
Table 5: Comparison of different embeddings on Weibo
['[BOLD] Approach', '[BOLD] P(%)', '[BOLD] R(%)', '[BOLD] F1(%)']
The FastText embedding and all BAN embeddings significantly outperform the random embedding. And BAN representation is competitive with the FastText embeding. These results indicate that our embedding captures useful information. Furthermore, BAN has a terrific advantage that the data required is much less than other pre-trained embeddings like FastText. The data used in our method is at least two orders of magnitude smaller than FastText. Moreover, BAN embeddinglast and BAN embeddingave achieve better performances than FastText, in terms of recall and F1-score. This experiment illustrates that back attention knowledge transferred from BAN has inherent semantic information and could transfer task-specific information.