SQL-to-Text Generation with Graph-to-Sequence Model

EMNLP 2018 Kun XuLingfei WuZhiguo WangYansong FengVadim Sheinin

Previous work approaches the SQL-to-text generation task using vanilla Seq2Seq models, which may not fully capture the inherent graph-structured information in SQL query. In this paper, we first introduce a strategy to represent the SQL query as a directed graph and then employ a graph-to-sequence model to encode the global structure information into node embeddings... (read more)

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