Enhanced Multi-Intent Recognition with BERT Embeddings and Graph-based Decoding

In the question-answering system, users frequently express multiple intents within a single utterance. The majority of intent recognition models tend to either primarily address single-intent scenarios or simply aggregate the overall intent context vectors of all tokens, neglecting the integration of multi-intent information. However, current methods suffer from slow inference speed, limited generalization capability, and the relatively independent treatment of multi-intent recognition tasks, leading to suboptimal model performance. In this paper, we design a novel model based on BERT, employing a non-autoregressive approach for the task of multi-intent recognition. This model achieves enhanced speed and accuracy. Additionally, this model introduces a global slot-intent interaction layer, simulating interactions between multiple intents and slots within an utterance. Experimental results on a series of benchmarking datasets demonstrate that this model outperforms the state-of-the-art methods, improving both the effectiveness and efficiency of the overall system.