This paper presents a neural architecture for Vietnamese sequence labeling tasks including part-of-speech (POS) tagging and named entity recognition (NER). We applied the model described in lample-EtAl:2016:N16-1 that is a combination of bidirectional Long-Short Term Memory and Conditional Random Fields, which rely on two sources of information about words: character-based word representations learned from the supervised corpus and pre-trained word embeddings learned from other unannotated corpora. Experiments on benchmark datasets show that this work achieves state-of-the-art performances on both tasks - 93.52 accuracy for POS tagging and 94.88 F1 for NER. Our sourcecode is available at here.
@cite_9: This paper demonstrates neural network-based toolkit namely NNVLP for essential Vietnamese language processing tasks including part-of-speech (POS) tagging, chunking, named entity recognition (NER). Our toolkit is a combination of bidirectional Long Short-Term Memory (Bi-LSTM), Convolutional Neural Network (CNN), Conditional Random Field (CRF), using pre-trained word embeddings as input, which achieves state-of-the-art results on these three tasks. We provide both API and web demo for this toolkit.
@cite_10: Comunicacio presentada a la 2016 Conference of the North American Chapter of the Association for Computational Linguistics, celebrada a San Diego (CA, EUA) els dies 12 a 17 de juny 2016.
@cite_15: 
In deep learning approach, our work is closely related to NNVLP @cite_9 . Being motivated by CNN-LSTM-CRF architecture that achieves state-of-the-art performance in many languages @cite_15 , NNVLP applied this architecture to Vietnamese. In our work, instead of CNNs, we use LSTM to represent character embeddings as in @cite_10 .