{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T17:13:22Z","timestamp":1772039602958,"version":"3.50.1"},"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":[[2018,7]]},"abstract":"<jats:p>Following great success in the image processing field, the idea of adversarial training has been applied to tasks in the natural language processing (NLP) field.\n\nOne promising approach directly applies adversarial training developed in the image processing field to the input word embedding space instead of the discrete input space of texts.\n\nHowever, this approach abandons such interpretability as generating adversarial texts to significantly improve the performance of NLP tasks.\n\nThis paper restores interpretability to such methods by restricting the directions of perturbations toward the existing words in the input embedding space.\n\nAs a result, we can straightforwardly reconstruct each input with perturbations to an actual text by considering the perturbations to be the replacement of words in the sentence while maintaining or even improving the task performance.<\/jats:p>","DOI":"10.24963\/ijcai.2018\/601","type":"proceedings-article","created":{"date-parts":[[2018,7,5]],"date-time":"2018-07-05T05:49:10Z","timestamp":1530769750000},"page":"4323-4330","source":"Crossref","is-referenced-by-count":86,"title":["Interpretable Adversarial Perturbation in Input Embedding Space for Text"],"prefix":"10.24963","author":[{"given":"Motoki","family":"Sato","sequence":"first","affiliation":[{"name":"Preferred Networks, Inc."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Suzuki","sequence":"additional","affiliation":[{"name":"NTT Communication Science Laboratories"},{"name":"RIKEN AIP"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hiroyuki","family":"Shindo","sequence":"additional","affiliation":[{"name":"Nara Institute of Science and Technology"},{"name":"RIKEN AIP"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuji","family":"Matsumoto","sequence":"additional","affiliation":[{"name":"Nara Institute of Science and Technology"},{"name":"RIKEN AIP"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"name":"Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}","theme":"Artificial Intelligence","location":"Stockholm, Sweden","acronym":"IJCAI-2018","number":"27","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2018,7,13]]},"end":{"date-parts":[[2018,7,19]]}},"container-title":["Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2018,7,5]],"date-time":"2018-07-05T05:54:21Z","timestamp":1530770061000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2018\/601"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2018,7]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2018\/601","relation":{},"subject":[],"published":{"date-parts":[[2018,7]]}}}