Jie Chen, Lois C. McInnes, et al.
Journal of Scientific Computing
Semantic parsing is a fundamental problem in natural language understanding, as it involves the mapping of natural language to structured forms such as executable queries or logic-like knowledge representations. Existing deep learning approaches for semantic parsing have shown promise on a variety of benchmark data sets, particularly on textto- SQL parsing. However, most text-to-SQL parsers do not generalize to unseen data sets in different domains. In this paper, we propose a new cross-domain learning scheme to perform text-to-SQL translation and demonstrate its use on Spider, a large-scale cross-domain text-to-SQL data set. We improve upon a state-of-the-art Spider model, SyntaxSQLNet, by constructing a graph of column names for all databases and using graph neural networks to compute their embeddings. The resulting embeddings offer better cross-domain representations and SQL queries, as evidenced by substantial improvement on the Spider data set compared to SyntaxSQLNet.
Jie Chen, Lois C. McInnes, et al.
Journal of Scientific Computing
Chul Sung, Tengfei Ma, et al.
EMNLP-IJCNLP 2019
Siyu Liao, Jie Chen, et al.
AAAI 2020
Yonggui Yan, Jie Chen, et al.
ICML 2023