Name
Abstract | ||
|---|---|---|
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. This model can effectively learn the correlation between the SQL query pattern and its interpretation. Experimental results on the WikiSQL dataset and Stackoverflow dataset show that our model significantly outperforms the Seq2Seq and Tree2Seq baselines, achieving the state-of-the-art performance. |
Year | Venue | DocType |
|---|---|---|
2018 | EMNLP | Journal |
Volume | Citations | PageRank |
abs/1809.05255 | 0 | 0.34 |
References | Authors | |
18 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| kun xu | 1 | 203 | 20.29 |
| Lingfei Wu | 2 | 116 | 32.05 |
| Zhiguo Wang | 3 | 354 | 24.64 |
| Yansong Feng | 4 | 735 | 64.17 |
| Vadim Sheinin | 5 | 38 | 10.07 |