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Process."],"published-print":{"date-parts":[[2021,11,30]]},"abstract":"<jats:p>Cross-lingual dialogue systems are increasingly important in e-commerce and customer service due to the rapid progress of globalization. In real-world system deployment, machine translation (MT) services are often used before and after the dialogue system to bridge different languages. However, noises and errors introduced in the MT process will result in the dialogue system's low robustness, making the system's performance far from satisfactory. In this article, we propose a novel MT-oriented noise enhanced framework that exploits multi-granularity MT noises and injects such noises into the dialogue system to improve the dialogue system's robustness. Specifically, we first design a method to automatically construct multi-granularity MT-oriented noises and multi-granularity adversarial examples, which contain abundant noise knowledge oriented to MT. Then, we propose two strategies to incorporate the noise knowledge: (i) Utterance-level adversarial learning and (ii) Knowledge-level guided method. The former adopts adversarial learning to learn a perturbation-invariant encoder, guiding the dialogue system to learn noise-independent hidden representations. The latter explicitly incorporates the multi-granularity noises, which contain the noise tokens and their possible correct forms, into the training and inference process, thus improving the dialogue system's robustness. Experimental results on three dialogue models, two dialogue datasets, and two language pairs have shown that the proposed framework significantly improves the performance of the cross-lingual dialogue system.<\/jats:p>","DOI":"10.1145\/3457571","type":"journal-article","created":{"date-parts":[[2021,8,12]],"date-time":"2021-08-12T16:57:58Z","timestamp":1628787478000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Robust Cross-lingual Task-oriented Dialogue"],"prefix":"10.1145","volume":"20","author":[{"given":"Lu","family":"Xiang","sequence":"first","affiliation":[{"name":"National Laboratory of Pattern Recognition, Institute of Automation, CAS, School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junnan","family":"Zhu","sequence":"additional","affiliation":[{"name":"National Laboratory of Pattern Recognition, Institute of Automation, CAS, School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yang","family":"Zhao","sequence":"additional","affiliation":[{"name":"National Laboratory of Pattern Recognition, Institute of Automation, CAS, School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu","family":"Zhou","sequence":"additional","affiliation":[{"name":"National Laboratory of Pattern Recognition, Institute of Automation, CAS, School of Artificial Intelligence, University of Chinese Academy of Sciences, Fanyu AI Research, Beijing Fanyu Technology Co., Ltd, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chengqing","family":"Zong","sequence":"additional","affiliation":[{"name":"National Laboratory of Pattern Recognition, Institute of Automation, CAS, School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,8,12]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D18-1316"},{"key":"e_1_2_1_2_1","volume-title":"Proceedings of the 27th International Conference on Computational Linguistics. 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In Proceedings of the International Conference on Learning Representations (ICLR'17) . Antoine Bordes and Jason Weston. 2017. Learning end-to-end goal-oriented dialog. In Proceedings of the International Conference on Learning Representations (ICLR'17)."},{"key":"e_1_2_1_6_1","volume-title":"Proceedings of the 17th Conference on Computational Natural Language Learning. Association for Computational Linguistics, 193\u2013201","author":"Calvo Marcos","year":"2013","unstructured":"Marcos Calvo , Fernando Garc\u00eda , Llu\u00eds- F. Hurtado , Santiago Jim\u00e9nez , and Emilio Sanchis . 2013 . Exploiting multiple hypotheses for multilingual spoken language understanding . In Proceedings of the 17th Conference on Computational Natural Language Learning. Association for Computational Linguistics, 193\u2013201 . Marcos Calvo, Fernando Garc\u00eda, Llu\u00eds-F. Hurtado, Santiago Jim\u00e9nez, and Emilio Sanchis. 2013. 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