{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T17:07:11Z","timestamp":1778605631642,"version":"3.51.4"},"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":[[2019,8]]},"abstract":"<jats:p>Natural language generation (NLG) is an essential component of task-oriented dialogue systems. Despite the recent success of neural approaches for NLG, they are typically developed for particular domains with rich annotated training examples. In this paper, we study NLG in a low-resource setting to generate sentences in new scenarios with handful training examples. We formulate the problem from a meta-learning perspective, and propose a generalized optimization-based approach (Meta-NLG) based on the well-recognized model-agnostic meta-learning (MAML) algorithm. Meta-NLG defines a set of meta tasks, and directly incorporates the objective of adapting to new low-resource NLG tasks into the meta-learning optimization process. Extensive experiments are conducted on a large multi-domain dataset (MultiWoz) with diverse linguistic variations. We show that Meta-NLG\u00a0significantly outperforms other training procedures in various low-resource configurations. We analyze the results, and demonstrate that Meta-NLG\u00a0adapts extremely fast and well to low-resource situations.<\/jats:p>","DOI":"10.24963\/ijcai.2019\/437","type":"proceedings-article","created":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T07:46:05Z","timestamp":1564299965000},"page":"3151-3157","source":"Crossref","is-referenced-by-count":25,"title":["Meta-Learning for Low-resource Natural Language Generation in Task-oriented Dialogue Systems"],"prefix":"10.24963","author":[{"given":"Fei","family":"Mi","sequence":"first","affiliation":[{"name":"Artificial Intelligence Laboratory, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL)"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Minlie","family":"Huang","sequence":"additional","affiliation":[{"name":"Institute for Artificial Intelligence, Beijing National Research Center for Information Science and Technology, Department of Computer Science and Technology, Tsinghua University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiyong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Depart of Automation, Hangzhou Dianzi University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Boi","family":"Faltings","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Laboratory, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL)"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"name":"Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}","theme":"Artificial Intelligence","location":"Macao, China","acronym":"IJCAI-2019","number":"28","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2019,8,10]]},"end":{"date-parts":[[2019,8,16]]}},"container-title":["Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T07:49:17Z","timestamp":1564300157000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2019\/437"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2019,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2019\/437","relation":{},"subject":[],"published":{"date-parts":[[2019,8]]}}}