A neural joint model for Vietnamese word segmentation, POS tagging and dependency parsing
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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.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 30 Dec 2018.
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Citing papers per year: 2021: 3; 2022: 0; 2023: 1; 2024: 1.
5 papers.
- 2021PhoNLP: A joint multi-task learning model for Vietnamese part-of-speech tagging, named entity recognition and dependency parsingcited by 13
- 2021Span Labeling Approach for Vietnamese and Chinese Word Segmentationcited by 2
- 2024An Attempt to Develop a Neural Parser based on Simplified Head-Driven Phrase Structure Grammar on Vietnamesecited by 2
- 2021Augmenting Part-of-speech Tagging with Syntactic Information for Vietnamese and Chinesecited by 0
- 2023Is word segmentation necessary for Vietnamese sentiment classification?cited by 0
- 2024An Attempt to Develop a Neural Parser based on Simplified Head-Driven Phrase Structure Grammar on Vietnamese26 Nov 2024
- 2023Is word segmentation necessary for Vietnamese sentiment classification?1 Jan 2023
- 2021Span Labeling Approach for Vietnamese and Chinese Word Segmentation1 Oct 2021
- 2021Augmenting Part-of-speech Tagging with Syntactic Information for Vietnamese and Chinese24 Feb 2021
- 2021PhoNLP: A joint multi-task learning model for Vietnamese part-of-speech tagging, named entity recognition and dependency parsing5 Jan 2021