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

2018/12/29 by Dat Quoc Nguyen, Nguyen, Dat Quoc · 1 citation
Computer Science · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Natural Language Processing Techniques #Speech Recognition and Synthesis #Topic Modeling

paper · pdf · doi:10.48550/arxiv.1812.11459

openalex publication_date 2018/12/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

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• Augmenting Part-of-speech Tagging with Syntactic Information for Vietnamese and Chinese

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