An intention multiple-representation model with expanded information. (July 2021)

• Record Type: Journal Article
• Authors: Hu, Jingxiang; Peng, Junjie; Zhang, Wenqiang; Qi, Lizhe; Hu, Miao; Zhang, Huanxiang
• Is Part Of: Computer speech & language. Volume 68(2021)
• Publication Date: 2021-07
• Subjects: Intention multiple-representation -- Intention recognition -- Information fusion -- Knowledge learning -- Self-attention; Speech processing systems -- Periodicals; Automatic speech recognition -- Periodicals; Computers -- Periodicals; Linguistics -- Periodicals; Speech-Language Pathology -- Periodicals; Traitement automatique de la parole -- Périodiques; Reconnaissance automatique de la parole -- Périodiques; Automatic speech recognition; Speech processing systems; Electronic journals; Periodicals; 006.454
• DOI: 10.1016/j.csl.2021.101196
• Languages: English
• ISSNs: 0885-2308

Abstract

Abstract: Short text is the main carrier for people to express their ideas and opinions. And it is very important as well a big challenge to understand the meaning of short text or recognize the semantic patterns of different short texts. Most of existing methods use word embedding and short text interaction to learn short text pairs semantic patterns. However, some of these methods are complicated and cannot fully capture the relations of words and the interaction of short text pairs. To solve this problem, a self-attention based model, that is, Knowledge Learning for Matching Question (KLMQ) is proposed. Using part-of-speech to mine the relations of words, and the model obtains the relations of short texts from the grammar, syntax and morphology. Meanwhile, it adopts information fusion strategy to enhance the interaction between short text pairs, which ensures the model works well with the expanded information, such as the order of words, the correlation of words and the relations of short text pairs. To verify the correctness of the proposed model and the efficiency of the expanded information, extensive experiments were carried out on public datasets. Experimental results show that the model performs better than that of traditional neural network models and the expanded information can much improve the performances of intention multiple-representation recognition.