BERT for Joint Intent Classification and Slot Filling

Understand

Intent classification and slot filling are two essential tasks for natural language understanding.

• They often suffer from small-scale human-labeled training data, resulting in poor generalization capability, especially for rare words.
• Recently a new language representation model, BERT (Bidirectional Encoder Representations from Transformers), facilitates pre-training deep bidirectional representations on large-scale unlabeled corpora, and has created state-of-the-art models for a wide variety of natural language processing tasks after simple fine-tuning.
• However, there has not been much effort on exploring BERT for natural language understanding.