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In this paper, we first introduce a strategy to\nrepresent the SQL query as a directed graph and then employ a graph-to-sequence\nmodel to encode the global structure information into node embeddings. This\nmodel can effectively learn the correlation between the SQL query pattern and\nits interpretation. Experimental results on the WikiSQL dataset and\nStackoverflow dataset show that our model significantly outperforms the Seq2Seq\nand Tree2Seq baselines, achieving the state-of-the-art performance.","url_abs":"http://arxiv.org/abs/1809.05255v2","url_pdf":"http://arxiv.org/pdf/1809.05255v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"sql-to-text-generation-with-graph-to-sequence","repo_url":"https://github.com/IBM/SQL-to-Text","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"graph-to-sequence","task_name":"Graph-to-Sequence"},{"task_slug":"sql-to-text","task_name":"SQL-to-Text"},{"task_slug":"text-generation","task_name":"Text Generation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1809.05255","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1809.05255"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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