LSTM Easy-first Dependency Parsing with Pre-trained Word Embeddings and Character-level Word Embeddings in Vietnamese
This work addresses dependency parsing for Vietnamese, an incremental improvement over existing neural methods.
The paper tackled Vietnamese dependency parsing by proposing an LSTM easy-first method with pre-trained and character-level word embeddings, achieving an unlabeled attachment score of 80.91% and a labeled attachment score of 72.98% on the Vietnamese Dependency Treebank.
In Vietnamese dependency parsing, several methods have been proposed. Dependency parser which uses deep neural network model has been reported that achieved state-of-the-art results. In this paper, we proposed a new method which applies LSTM easy-first dependency parsing with pre-trained word embeddings and character-level word embeddings. Our method achieves an accuracy of 80.91% of unlabeled attachment score and 72.98% of labeled attachment score on the Vietnamese Dependency Treebank (VnDT).