{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/bert-for-joint-intent-classification-and-slot","title":"BERT for Joint Intent Classification and Slot Filling","arxiv_id":"1902.10909","date":"2019-02-28","proceeding":null,"authors":["Qian Chen","Zhu Zhuo","Wen Wang"],"abstract":"Intent classification and slot filling are two essential tasks for natural\nlanguage understanding. They often suffer from small-scale human-labeled\ntraining data, resulting in poor generalization capability, especially for rare\nwords. Recently a new language representation model, BERT (Bidirectional\nEncoder Representations from Transformers), facilitates pre-training deep\nbidirectional representations on large-scale unlabeled corpora, and has created\nstate-of-the-art models for a wide variety of natural language processing tasks\nafter simple fine-tuning. However, there has not been much effort on exploring\nBERT for natural language understanding. In this work, we propose a joint\nintent classification and slot filling model based on BERT. Experimental\nresults demonstrate that our proposed model achieves significant improvement on\nintent classification accuracy, slot filling F1, and sentence-level semantic\nframe accuracy on several public benchmark datasets, compared to the\nattention-based recurrent neural network models and slot-gated models.","url_abs":"http://arxiv.org/abs/1902.10909v1","url_pdf":"http://arxiv.org/pdf/1902.10909v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 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