# A neural joint model for Vietnamese word segmentation, POS tagging and dependency parsing

arXiv: https://arxiv.org/abs/1812.11459 · HTML: https://mlsanity.com/paper/1812.11459/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Dec 2018.

## Built on by (most cited first, 5 of 5)

- [PhoNLP: A joint multi-task learning model for Vietnamese part-of-speech tagging, named entity recognition and dependency parsing](https://mlsanity.com/paper/2101.01476/) (2021)
- [Span Labeling Approach for Vietnamese and Chinese Word Segmentation](https://mlsanity.com/paper/2110.00156/) (2021)
- [An Attempt to Develop a Neural Parser based on Simplified Head-Driven Phrase Structure Grammar on Vietnamese](https://mlsanity.com/paper/2411.17270/) (2024)
- [Augmenting Part-of-speech Tagging with Syntactic Information for Vietnamese and Chinese](https://mlsanity.com/paper/2102.12136/) (2021)
- [Is word segmentation necessary for Vietnamese sentiment classification?](https://mlsanity.com/paper/2301.00418/) (2023)

## Abstract (arXiv)

We propose the first multi-task learning model for joint Vietnamese word segmentation, part-of-speech (POS) tagging and dependency parsing. In particular, our model extends the BIST graph-based dependency parser (Kiperwasser and Goldberg, 2016) with BiLSTM-CRF-based neural layers (Huang et al., 2015) for word segmentation and POS tagging. On Vietnamese benchmark datasets, experimental results show that our joint model obtains state-of-the-art or competitive performances.