Intent recognition model based on sequential information and sentence features

Abstract

In human–computer dialogue systems, intent recognition is crucial for determining the intentions or purposes of users during interactions with the system, enabling the system to provide appropriate responses or actions. This paper proposes an intent recognition model that integrates sequential information and sentence structural features. Specifically, the approach utilizes a CNN to capture local salient features in the text, followed by a BILSTM to extract sequential information within the local context. The sequential information is then fed into a multi-head attention mechanism to focus on more relevant sequential details. Additionally, the original data is processed by BERT to extract sentence structural features. Finally, the sequential information features and sentence structural features are concatenated and fused to achieve enhanced intent recognition performance. This approach effectively leverages contextual and semantic information within the text, leading to improved accuracy in intent recognition. Experimental results demonstrate the effectiveness of the proposed method in intent recognition and its high relevance for practical applications.