Name
Abstract | ||
|---|---|---|
We present BRIDGE, a powerful sequential architecture for modeling dependencies between natural language questions and relational databases in cross-DB semantic parsing. BRIDGE represents the question and DB schema in a tagged sequence where a subset of the fields are augmented with cell values mentioned in the question. The hybrid sequence is encoded by BERT with minimal subsequent layers and the text-DB contextualization is realized via the fine-tuned deep attention in BERT. Combined with a pointer-generator decoder with schema-consistency driven search space pruning, BRIDGE attained state-of-the-art performance on popular cross-DB text-to-SQL benchmarks, Spider (71.1\% dev, 67.5\% test with ensemble model) and WikiSQL (92.6\% dev, 91.9\% test). Our analysis shows that BRIDGE effectively captures the desired cross-modal dependencies and has the potential to generalize to more text-DB related tasks. Our implementation is available at \url{https://github.com/salesforce/TabularSemanticParsing}. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.18653/V1/2020.FINDINGS-EMNLP.438 | EMNLP |
DocType | Volume | Citations |
Conference | 2020.findings-emnlp | 0 |
PageRank | References | Authors |
0.34 | 24 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Victoria Lin | 1 | 45 | 3.39 |
| Richard Socher | 2 | 6770 | 230.61 |
| Caiming Xiong | 3 | 969 | 69.56 |