The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
A Pilot Study of Text-to-SQL Semantic Parsing for Vietnamese
Open accessAbstract
Semantic parsing is an important NLP task.However, Vietnamese is a low-resource language in this research area.In this paper, we present the first public large-scale Text-to-SQL semantic parsing dataset for Vietnamese.We extend and evaluate two strong semantic parsing baselines EditSQL (Zhang et al., 2019) and IRNet (Guo et al., 2019) on our dataset.We compare the two baselines with key configurations and find that: automatic Vietnamese word segmentation improves the parsing results of both baselines; the normalized pointwise mutual information (NPMI) score (Bouma, 2009) is useful for schema linking; latent syntactic features extracted from a neural dependency parser for Vietnamese also improve the results; and the monolingual language model PhoBERT for Vietnamese (Nguyen and Nguyen, 2020) helps produce higher performances than the recent best multilingual language model XLM-R (Conneau et al., 2020).
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805 · Cited by 73,173
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 54,325
Unsupervised Cross-lingual Representation Learning at Scale
Conneau, A., Khandelwal, K., Goyal, N., Chaudhary, V., Wenzek, G., Guzmán, F., Grave, E., Ott, M., Zettlemoyer, L., & Stoyanov, V. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.747 · Cited by 5,637
A Syntactic Neural Model for General-Purpose Code Generation
Yin, P., & Neubig, G. (2017) · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p17-1041 · Cited by 623
A Syntactic Neural Model for General-Purpose Code Generation
Yin, P., & Neubig, G. (2017) · arXiv · doi:10.48550/arxiv.1704.01696 · Cited by 585
Metadata
- Authors
- Tuan Nguyen, A., Dao, M. H., & Nguyen, D. Q.
- Venue
- Findings of the Association for Computational Linguistics: EMNLP 2020
- Published
- 2020
- Pages
- 4079-4085
- OpenAlex
- W3091857408
- Cited by
- 52
- References
- 5
- Citations by year
- 2026: 4 · 2025: 7 · 2024: 7 · 2023: 12 · 2022: 11 · 2021: 10 · 2020: 1
- FWCI
- 3.18
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Keywords
- text-to-SQL, semantic parsing, Vietnamese, low-resource languages, dataset construction, word segmentation, schema linking, pointwise mutual information, dependency parsing, PhoBERT, multilingual language models
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