This paper explores data augmentation\u2014a technique particularly suitable for training with limited data\u2014for highly multiclass text classification tasks, which have a large number of output classes. On four diverse highly multi-class tasks, we find that well-known data augmentation techniques (Sennrich et al., 2016b;Wang et al., 2018; Wei and Zou, 2019) can improve performance by up to 3.0% on average. To further boost performance, we present a simple training strategy called curriculum data augmentation, which leverages curriculum learning by first training on only original examples and then introducing augmented data as training progresses. We explore a two-stage and a gradual schedule, and find that, compared with standard single-stage training, curriculum data augmentation improves performance, trains faster, and maintains robustness high augmentation temperatures (strengths)<\/p>\n","protected":false},"excerpt":{"rendered":"
This paper explores data augmentation\u2014a technique particularly suitable for training with limited data\u2014for highly multiclass text classification tasks, which have a large number of output classes. On four diverse highly multi-class tasks, we find that well-known data augmentation techniques (Sennrich et al., 2016b;Wang et al., 2018; Wei and Zou, 2019) can improve performance by up […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":null,"msr_publishername":"","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"","msr_pages_string":"","msr_page_range_start":"","msr_page_range_end":"","msr_series":"","msr_volume":"","msr_copyright":"","msr_conference_name":"19th Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 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