Adaptive selection: External knowledge and internal context representation for few-shot intention recognition

Intention recognition models usually need to be trained on a large number of data, and when new intentions appear in the discriminant field, only a small amount of data is available. The method based on few-shot learning can well deal with this problem. In existing methods, the intent representation is often obtained by summing or averaging the sample representations. This will make the distance between the intents too close and cause the classification to fail. In this paper, we propose a few-shot intent recognition method based on adaptive selection of external knowledge and internal context representation. This method combines external knowledge representation extracted from a knowledge graph with internal context representation within sentences. It achieves hierarchical modeling of sentences through a dynamic routing algorithm and adaptively selects entity semantic representation information to enhance the semantic representation of entities in sentences. Experiments conducted on the Banking 77 dataset demonstrate that this method outperforms existing methods in both cross-domain and same-domain scenarios, particularly achieving an accuracy rate of 80.73 % under the 3-way 30-shot setting.