System Architecture
We implement the deep neural network model described in BIBREF5 . This model is a combination of Bi-directional Long Short-Term Memory (Bi-LSTM), Convolutional Neural Network (CNN), and Conditional Random Field (CRF). In particular, this model takes as input a sequence of the concatenation of word embedding pre-trained by word2vec tool and character-level word feature trained by CNN. That sequence is then passed to a Bi-LSTM, and then a CRF layer takes as input the output of the Bi-LSTM to predict the best named entity output sequence. Figure FIGREF9 and Figure FIGREF10 describe the architectures of BI-LSTM-CRF layers, and CNN layer respectively.
NNVLP toolkit uses these architectures for all tasks including POS tagging, chunking, and NER. Because each word in the Vietnamese language may consist of more than one syllables with spaces in between, which could be regarded as multiple words by the unsupervised models, we, first, segment the input texts into sequences of words by pyvi toolkit. These word sequences are put into NNVLP toolkit to get corresponding POS tag sequences. Next, these words and POS tag sequences are put into NNVLP toolkit to get corresponding chunk sequences. Finally, NNVLP toolkit takes as input sequences of the concatenation of word, POS tag, and chunk to predict corresponding NER sequences. Figure FIGREF12 presents this pipeline of NNVLP toolkit.
NNVLP API
NNVLP API is an API for Vietnamese Language Processing which takes input sentences and outputs a JSON containing a list of sentences where each word in these sentences has POS tag, chunk, named entity attributes as shown in Figure FIGREF15 .
Web Demo
We also provide web interface for users of NNVLP toolkit. Users can type or paste raw texts into the textbox and click Submit button to get the corressponding POS tag, chunk, named entity sequences. Each label is tagged with different color to make the output easy to see. Users can also look up the meaning of each label by click Help button. Figure FIGREF19 presents the web interface of our system.
Data Sets
To compare fairly, we train and evaluate these systems on the VLSP corpora. In particular, we conduct experiments on Viet Treebank corpus for POS tagging and chunking tasks, and on VLSP shared task 2016 corpus for NER task. All of these corpora are converted to CoNLL format. The corpus of POS tagging task consists of two columns namely word, and POS tag. For chunking task, there are three columns namely word, POS tag, and chunk in the corpus. The corpus of NER task consists of four columns. The order of these columns are word, POS tag, chunk, and named entity. While NER corpus has been separated into training and testing parts, the POS tagging and chunking data sets are not previously divided. For this reason, we use INLINEFORM0 of these data sets as a training set, and the remaining as a testing set. Because our system adopts early stopping method, we use INLINEFORM1 of these data sets from the training set as a development set when training NNVLP system. Table TABREF24 and Table TABREF25 shows the statistics of each corpus.
Evaluation Methods
We use the accuracy score that is the percentage of correct labels to evaluate the performance of each system for POS tagging task. For chunking and NER tasks, the performance is measured with INLINEFORM0 score, where INLINEFORM1 . Precision ( INLINEFORM2 ) is the percentage of chunks or named entities found by the learning system that are correct. Recall ( INLINEFORM3 ) is the percentage of chunks or named entities present in the corpus that are found by the system. A chunk or named entity is correct only if it is an exact match of the corresponding phrase in the data file.
Experiment Results
We evaluate performances of our system and several published systems on POS tagging, chunking, and NER data sets. Inputs for POS tagging task are words, for chunking task are words and POS tags, and for NER task are words, POS tags, and chunks. Table TABREF30 , Table TABREF32 , and Table TABREF33 present the performance of each system on POS tagging, chunking, and NER task respectively. The hyper-parameters for training NNVLP are given in Table TABREF31 .
By combining Bi-directional Long Short-Term Memory, Convolutional Neural Network, and Conditional Random Field, our system outperforms most published systems on these three tasks. In particular, NNVLP toolkit achieves an accuracy of INLINEFORM0 , INLINEFORM1 scores of INLINEFORM2 and INLINEFORM3 for POS tagging, chunking, and NER tasks respectively.
Conclusion
We present a neural network-based toolkit for Vietnamese processing that is a combination of Bi-LSTM, CNN, and CRF. The system takes raw sentences as input and produces POS tag, chunk and named entity annotations for these sentences. The experimental results showed that NNVLP toolkit achieves state-of-the-art results on three tasks including POS tagging, chunking, and NER.

Question: What datasets do they use for the tasks?
Answer:  Viet Treebank corpus for POS tagging and chunking tasks, and on VLSP shared task 2016 corpus for NER task