{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/st-sql-semi-supervised-self-training-for-text","title":"ST-SQL: Semi-Supervised Self-Training for Text-to-SQL via Column Specificity Meta-Learning","arxiv_id":null,"date":"2021-11-16","proceeding":"ACL ARR November 2021 11","authors":["Anonymous"],"abstract":"The few-shot problem is an urgent challenge for the generalization capability of the single-table text-to-SQL task. Current few-shot methods neglect the potential information of unlabeled data and have a domain bias due to the same weight of samples. Motivated by this, this paper proposes a Self-Training text-to-SQL (ST-SQL) method which handles the problem from both views of data and algorithms. At the data level, ST-SQL performs data expansion by using an iterative framework to attach pseudo-labels to unlabeled data. The expanded data are sampled to reversely train the model. At the algorithm level, ST-SQL defines a column specificity to perform a more fine-grained gradient update during meta-training. The common samples are attached more weight to eliminate the domain bias. ST-SQL achieves state-of-the-art results on both open-domain and domain-specific benchmarks and brings more significant improvements on few-shot tests.","url_abs":"https://openreview.net/forum?id=CPdvNQGrOr0","url_pdf":"https://openreview.net/pdf?id=CPdvNQGrOr0","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":"st-sql-semi-supervised-self-training-for-text","repo_url":"https://github.com/ygxw0909/st-sql","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"meta-learning","task_name":"Meta-Learning"},{"task_slug":"specificity","task_name":"Specificity"},{"task_slug":"text-to-sql","task_name":"Text to SQL"},{"task_slug":"text-to-sql","task_name":"Text-To-SQL"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}