{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T07:42:38Z","timestamp":1723016558325},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,7]]},"abstract":"<jats:p>The few-shot problem is an urgent challenge for single-table text-to-SQL. Existing methods ignore the potential value of unlabeled data, and merely rely on a coarse-grained Meta-Learning (ML) algorithm that neglects the differences of column contributions to the optimization object. This paper proposes a Meta Self-Training text-to-SQL (MST-SQL) method to solve the problem. Specifically, MST-SQL is based on column-wise HydraNet and adopts self-training as an effective mechanism to learn from readily available unlabeled samples. During each epoch of training, it first predicts pseudo-labels for unlabeled samples and then leverages them to update the parameters. A fine-grained ML algorithm is used in updating, which weighs the contribution of columns by their specificity, in order to further improve the generalizability. Extensive experimental results on both open-domain and domain-specific benchmarks reveal that our MST-SQL has significant advantages in few-shot scenarios,  and is also competitive in standard supervised settings.<\/jats:p>","DOI":"10.24963\/ijcai.2022\/576","type":"proceedings-article","created":{"date-parts":[[2022,7,16]],"date-time":"2022-07-16T02:55:56Z","timestamp":1657940156000},"page":"4150-4156","source":"Crossref","is-referenced-by-count":1,"title":["Improving Few-Shot Text-to-SQL with Meta Self-Training via Column Specificity"],"prefix":"10.24963","author":[{"given":"Xinnan","family":"Guo","sequence":"first","affiliation":[{"name":"Southeast University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongrui","family":"Chen","sequence":"additional","affiliation":[{"name":"Southeast University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guilin","family":"Qi","sequence":"additional","affiliation":[{"name":"Southeast University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tianxing","family":"Wu","sequence":"additional","affiliation":[{"name":"Southeast University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao","family":"Xu","sequence":"additional","affiliation":[{"name":"Zhejiang Lab"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"number":"31","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2022","name":"Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}","start":{"date-parts":[[2022,7,23]]},"theme":"Artificial Intelligence","location":"Vienna, Austria","end":{"date-parts":[[2022,7,29]]}},"container-title":["Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T11:10:28Z","timestamp":1658142628000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2022\/576"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2022,7]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2022\/576","relation":{},"subject":[],"published":{"date-parts":[[2022,7]]}}}