neuralVietNLP: neural NLP Tools for Vietnamese Language Processing
Bang 4: Performance of the NER task using different embedding models. The MULTIWC_F_E_B is the concatenation of four embeddings: W2V_C2V, fastText, ELMO, and Bert_Base. “wemb dim” is the dimension of the embedding model. VnCoreNLP* means we retrain the VnCoreNLP with our pre-trained embeddings.
['[EMPTY]', 'F1', 'wemb dim', 'cemb dim', 'drpt', 'lstm-s', 'lrate']
Main results: It clearly shows that, by using the pre-trained embeddings on Vietnamese Wikipedia data, we can achieve the new state-of-the-art results on the task. The reason might be that FastText, ELMO and MULTI can handle OOV words as well as capture better the context of the words. Moreover, learning the embeddings from a formal dataset like Wikipedia is beneficial for the NER task. This also verified the fact that using our pre-trained embeddings on VnCoreNLP helps significantly boost its performance. This might indicate that using word analogy to select embeddings for downstream NLP tasks is sensible.