Editing-Based SQL Query Generation for Cross-Domain Context-Dependent Questions
Table 5: SParC results. For our models, we only report test set results of our best model on the dev set. ∗We improve the CD-Seq2Seq performance over yu2019sparc by separating and parsing the column names (e.g., stu_fname → student first name) and using the schema-specific output vocabulary during decoding.
['[EMPTY]', 'Question Match', 'Question Match', 'Interaction Match', 'Interaction Match']
SParC. Similar to Spider, our model without previous query as input already outperforms SyntaxSQL-con, achieving 31.4% question matching accuracy and 14.7% interaction matching accuracy. In addition, compared with CD-Seq2Seq, our model enjoys the benefits of the table-utterance encoder, turn attention, and the joint consideration of utterances and table schemas during the decoding stage. This boosts the performance by 10% question accuracy and 6% interaction accuracy. This shows the editing approach is indeed helpful to improve the generation quality when the previous query is the oracle. This improves over our vanilla BERT model without query attention and achieves 42.7% question and 21.6% interaction matching accuracy. With query editing, our best model further improves to 47.2% question and 29.5% interaction matching accuracy. This demonstrates the effectiveness of our query attention and query editing separately, both of which are essential to make use of the previous query.