{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,15]],"date-time":"2025-11-15T10:30:38Z","timestamp":1763202638505},"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>Despite the success of text-to-text pre-trained models in various natural language generation (NLG) tasks, the generation performance is largely restricted by the number of labeled data in downstream tasks, particularly in data-to-text generation tasks. Existing works mostly utilize abundant unlabeled structured data to conduct unsupervised pre-training for task adaption, which fail to model the complex relationship between source structured data and target texts. Thus, we introduce self-training as a better few-shot learner than task-adaptive pre-training, which explicitly captures this relationship via pseudo-labeled data generated by the pre-trained model. To alleviate the side-effect of low-quality pseudo-labeled data during self-training, we propose a novel method called Curriculum-Based Self-Training (CBST) to effectively leverage unlabeled data in a rearranged order determined by the difficulty of text generation. Experimental results show that our method can outperform fine-tuning and task-adaptive pre-training methods, and achieve state-of-the-art performance in the few-shot setting of data-to-text generation.<\/jats:p>","DOI":"10.24963\/ijcai.2022\/580","type":"proceedings-article","created":{"date-parts":[[2022,7,16]],"date-time":"2022-07-16T02:55:56Z","timestamp":1657940156000},"page":"4178-4184","source":"Crossref","is-referenced-by-count":3,"title":["Curriculum-Based Self-Training Makes Better Few-Shot Learners for Data-to-Text Generation"],"prefix":"10.24963","author":[{"given":"Pei","family":"Ke","sequence":"first","affiliation":[{"name":"CoAI Group, DCST, IAI, BNRIST, Tsinghua University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haozhe","family":"Ji","sequence":"additional","affiliation":[{"name":"CoAI Group, DCST, IAI, BNRIST, Tsinghua University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhenyu","family":"Yang","sequence":"additional","affiliation":[{"name":"OPPO Mobile Telecommunications Corp., Ltd, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi","family":"Huang","sequence":"additional","affiliation":[{"name":"JIUTIAN Team, China Mobile Research Institute, Beijing 100053, China"},{"name":"Tsinghua University-China Mobile Communications Group Co., Ltd. Joint Institute, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junlan","family":"Feng","sequence":"additional","affiliation":[{"name":"JIUTIAN Team, China Mobile Research Institute, Beijing 100053, China"},{"name":"Tsinghua University-China Mobile Communications Group Co., Ltd. Joint Institute, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoyan","family":"Zhu","sequence":"additional","affiliation":[{"name":"CoAI Group, DCST, IAI, BNRIST, Tsinghua University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Minlie","family":"Huang","sequence":"additional","affiliation":[{"name":"CoAI Group, DCST, IAI, BNRIST, Tsinghua University, Beijing, China"},{"name":"Tsinghua University-China Mobile Communications Group Co., Ltd. Joint Institute, Beijing, China"}],"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":"2022-07-18T11:10:29Z","timestamp":1658142629000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2022\/580"}},"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\/580","relation":{},"subject":[],"published":{"date-parts":[[2022,7]]}}}