  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).
