{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T18:08:28Z","timestamp":1772042908487,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":28,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,7,11]],"date-time":"2021-07-11T00:00:00Z","timestamp":1625961600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100000001","name":"NSF (National Science Foundation)","doi-asserted-by":"publisher","award":["III-1763325, III-1909323, and SaTC-1930941"],"award-info":[{"award-number":["III-1763325, III-1909323, and SaTC-1930941"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,7,11]]},"DOI":"10.1145\/3404835.3462995","type":"proceedings-article","created":{"date-parts":[[2021,7,12]],"date-time":"2021-07-12T02:41:48Z","timestamp":1626057708000},"page":"2005-2009","update-policy":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":14,"title":["Pseudo Siamese Network for Few-shot Intent Generation"],"prefix":"10.1145","author":[{"given":"Congying","family":"Xia","sequence":"first","affiliation":[{"name":"University of Illinois at Chicago, Chicago, IL, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Caiming","family":"Xiong","sequence":"additional","affiliation":[{"name":"Salesforce Research, Palo Alto, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Philip","family":"Yu","sequence":"additional","affiliation":[{"name":"University of Illinois at Chicago, Chicago, IL, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,7,11]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"Jamie Ryan Kiros, and Geoffrey E Hinton","author":"Ba Jimmy Lei","year":"2016","unstructured":"Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton. 2016. Layer normalization. arXiv preprint arXiv:1607.06450 (2016)."},{"key":"e_1_3_2_2_2_1","volume-title":"Generating sentences from a continuous space. arXiv preprint arXiv:1511.06349","author":"Bowman Samuel R","year":"2015","unstructured":"Samuel R Bowman, Luke Vilnis, Oriol Vinyals, Andrew M Dai, Rafal Jozefowicz, and Samy Bengio. 2015. Generating sentences from a continuous space. arXiv preprint arXiv:1511.06349 (2015)."},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"crossref","unstructured":"Yun-Nung Chen Dilek Hakkani-T\u00fcr G\u00f6khan T\u00fcr Jianfeng Gao and Li Deng. 2016. End-to-End Memory Networks with Knowledge Carryover for Multi-Turn Spoken Language Understanding.. In INTERSPEECH. 3245--3249.","DOI":"10.21437\/Interspeech.2016-312"},{"key":"e_1_3_2_2_4_1","unstructured":"Alice Coucke Alaa Saade Adrien Ball Th\u00e9odore Bluche Alexandre Caulier David Leroy Cl\u00e9ment Doumouro Thibault Gisselbrecht Francesco Caltagirone Thibaut Lavril et al. 2018. Snips voice platform: an embedded spoken language understanding system for private-by-design voice interfaces. arXiv preprint arXiv:1805.10190 (2018)."},{"key":"e_1_3_2_2_5_1","volume-title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In NAACL. 4171--4186.","author":"Devlin Jacob","year":"2019","unstructured":"Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In NAACL. 4171--4186."},{"key":"e_1_3_2_2_6_1","volume-title":"Unified Language Model Pre-training for Natural Language Understanding and Generation. arXiv preprint arXiv:1905.03197","author":"Dong Li","year":"2019","unstructured":"Li Dong, Nan Yang, Wenhui Wang, Furu Wei, Xiaodong Liu, Yu Wang, Jianfeng Gao, Ming Zhou, and Hsiao-Wuen Hon. 2019. Unified Language Model Pre-training for Natural Language Understanding and Generation. arXiv preprint arXiv:1905.03197 (2019)."},{"key":"e_1_3_2_2_7_1","volume-title":"Long short-term memory. Neural computation","author":"Hochreiter Sepp","year":"1997","unstructured":"Sepp Hochreiter and J\u00fcrgen Schmidhuber. 1997. Long short-term memory. Neural computation, Vol. 9, 8 (1997), 1735--1780."},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.5555\/3305381.3305545"},{"key":"e_1_3_2_2_9_1","volume-title":"Danilo Jimenez Rezende, and Max Welling","author":"Kingma Durk P","year":"2014","unstructured":"Durk P Kingma, Shakir Mohamed, Danilo Jimenez Rezende, and Max Welling. 2014. Semi-supervised Learning with Deep Generative Models. In Advances in Neural Information Processing Systems 27, Z. Ghahramani, M. Welling, C. Cortes, N. D. Lawrence, and K. Q. Weinberger (Eds.). Curran Associates, Inc., 3581--3589. https:\/\/2.zoppoz.workers.dev:443\/http\/papers.nips.cc\/paper\/5352-semi-supervised-learning-with-deep-generative-models.pdf"},{"key":"e_1_3_2_2_10_1","volume-title":"Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114","author":"Kingma Diederik P","year":"2013","unstructured":"Diederik P Kingma and Max Welling. 2013. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013)."},{"key":"e_1_3_2_2_11_1","volume-title":"Proceedings of the 44th international ACM SIGIR conference on Research and development in information retrieval .","author":"Liu Zhiwei","unstructured":"Zhiwei Liu, Ziwei Fan, Yu Wang, and Philip S. Yu. 2021. Augmenting Sequential Recommendation with Pseudo-PriorItems via Reversely Pre-training Transformer. Proceedings of the 44th international ACM SIGIR conference on Research and development in information retrieval ."},{"key":"e_1_3_2_2_12_1","volume-title":"Controlled Text Generation for Data Augmentation in Intelligent Artificial Agents. arXiv preprint arXiv:1910.03487","author":"Malandrakis Nikolaos","year":"2019","unstructured":"Nikolaos Malandrakis, Minmin Shen, Anuj Goyal, Shuyang Gao, Abhishek Sethi, and Angeliki Metallinou. 2019. Controlled Text Generation for Data Augmentation in Intelligent Artificial Agents. arXiv preprint arXiv:1910.03487 (2019)."},{"key":"e_1_3_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.findings-emnlp.108"},{"key":"e_1_3_2_2_14_1","unstructured":"Jake Snell Kevin Swersky and Richard Zemel. 2017. Prototypical networks for few-shot learning. In Advances in Neural Information Processing Systems. 4077--4087."},{"key":"e_1_3_2_2_15_1","unstructured":"Ashish Vaswani Noam Shazeer Niki Parmar Jakob Uszkoreit Llion Jones Aidan N Gomez \u0141ukasz Kaiser and Illia Polosukhin. 2017. Attention is all you need. In Advances in neural information processing systems. 5998--6008."},{"key":"e_1_3_2_2_16_1","volume-title":"Eda: Easy data augmentation techniques for boosting performance on text classification tasks. arXiv preprint arXiv:1901.11196","author":"Wei Jason W","year":"2019","unstructured":"Jason W Wei and Kai Zou. 2019. Eda: Easy data augmentation techniques for boosting performance on text classification tasks. arXiv preprint arXiv:1901.11196 (2019)."},{"key":"e_1_3_2_2_17_1","unstructured":"Yonghui Wu Mike Schuster Zhifeng Chen Quoc V Le Mohammad Norouzi Wolfgang Macherey Maxim Krikun Yuan Cao Qin Gao Klaus Macherey et al. 2016. Google's neural machine translation system: Bridging the gap between human and machine translation. arXiv preprint arXiv:1609.08144 (2016)."},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.findings-emnlp.303"},{"key":"e_1_3_2_2_19_1","volume-title":"Incremental Few-shot Text Classification with Multi-round New Classes: Formulation, Dataset and System. arXiv preprint arXiv:2104.11882","author":"Xia Congying","year":"2021","unstructured":"Congying Xia, Wenpeng Yin, Yihao Feng, and Philip Yu. 2021. Incremental Few-shot Text Classification with Multi-round New Classes: Formulation, Dataset and System. arXiv preprint arXiv:2104.11882 (2021)."},{"key":"e_1_3_2_2_20_1","volume-title":"2020 b. CG-BERT: Conditional Text Generation with BERT for Generalized Few-shot Intent Detection. arXiv preprint arXiv:2004.01881","author":"Xia Congying","year":"2020","unstructured":"Congying Xia, Chenwei Zhang, Hoang Nguyen, Jiawei Zhang, and Philip Yu. 2020 b. CG-BERT: Conditional Text Generation with BERT for Generalized Few-shot Intent Detection. arXiv preprint arXiv:2004.01881 (2020)."},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D18-1348"},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/CogMI50398.2020.00031"},{"key":"e_1_3_2_2_23_1","volume-title":"Proceedings of the Tenth International Workshop on Spoken Dialogue Systems Technology (IWSDS). Springer, Ortigia, Siracusa (SR), Italy, xxx--xxx. https:\/\/2.zoppoz.workers.dev:443\/http\/www.xx.xx\/xx\/","author":"Xingkun Liu Pawel Swietojanski","year":"2019","unstructured":"Pawel Swietojanski Xingkun Liu, Arash Eshghi and Verena Rieser. 2019. Benchmarking Natural Language Understanding Services for building Conversational Agents. In Proceedings of the Tenth International Workshop on Spoken Dialogue Systems Technology (IWSDS). Springer, Ortigia, Siracusa (SR), Italy, xxx--xxx. https:\/\/2.zoppoz.workers.dev:443\/http\/www.xx.xx\/xx\/"},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/3308558.3313644"},{"key":"e_1_3_2_2_25_1","unstructured":"Puyang Xu and Ruhi Sarikaya. 2013. Convolutional neural network based triangular crf for joint intent detection and slot filling. In ASRU. 78--83."},{"key":"e_1_3_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33017402"},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.emnlp-main.411"},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P17-1061"}],"event":{"name":"SIGIR '21: The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval","location":"Virtual Event Canada","acronym":"SIGIR '21","sponsor":["SIGIR ACM Special Interest Group on Information Retrieval"]},"container-title":["Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval"],"original-title":[],"link":[{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/dl.acm.org\/doi\/10.1145\/3404835.3462995","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/dl.acm.org\/doi\/pdf\/10.1145\/3404835.3462995","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/dl.acm.org\/doi\/pdf\/10.1145\/3404835.3462995","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:18:20Z","timestamp":1750191500000},"score":1,"resource":{"primary":{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/dl.acm.org\/doi\/10.1145\/3404835.3462995"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,11]]},"references-count":28,"alternative-id":["10.1145\/3404835.3462995","10.1145\/3404835"],"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1145\/3404835.3462995","relation":{},"subject":[],"published":{"date-parts":[[2021,7,11]]},"assertion":[{"value":"2021-07-11","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}