A Pilot Study of Text-to-SQL Semantic Parsing for Vietnamese

Anh Tuan Nguyen, Mai Hoang Dao, Dat Quoc Nguyen


Abstract
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).
Anthology ID:
2020.findings-emnlp.364
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2020
Month:
November
Year:
2020
Address:
Online
Venues:
EMNLP | Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4079–4085
Language:
URL:
https://aclanthology.org/2020.findings-emnlp.364
DOI:
10.18653/v1/2020.findings-emnlp.364
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2020.findings-emnlp.364.pdf