Intention classification in multiturn dialogue systems with key sentences mining. (8th June 2020)

• Record Type: Journal Article
• Authors: Cao, Bin; Ma, Kui; Liu, Yuqi; Xu, Yueshen; Zhu, Linan
• Other Names: Li Ying guestEditor.; Shyamasundar R.K. guestEditor.; Wang Xinheng guestEditor.
• Is Part Of: Computational intelligence. Volume 37:Number 2(2021)
• Page Start: 758
• Page End: 773
• Publication Date: 2020-06-08
• Subjects: key sentence -- keywords extraction -- multiturn dialogue -- text classification -- weight computation; Artificial intelligence -- Periodicals; Computational linguistics -- Periodicals; 006.3
• DOI: 10.1111/coin.12345
• Languages: English
• ISSNs: 0824-7935

Abstract

Abstract: The multiturn dialogue system has been prevalently used in e‐commerce websites and modern information systems, which significantly improves the efficiency of problem solving and further promotes the service quality. In a multiturn dialogue system, the problem of intention classification is a core task, as the intention of a customer is the basis of subsequent problems handling. However, traditional related methods are unsuitable for the classification of multiturn dialogues. Because traditional methods do not distinguish the importance of each sentence and concatenate all sentences in the text, which is likely to generate a model with low prediction accuracy. In this paper, we propose a method of multiturn dialogue classification based on key sentences mining. We design a keywords extraction algorithm, mining key sentences from the dialogue text. We propose an algorithm finishing the computation of the weights of each sentence. According to the sentence weight and the sentence vector, the dialogue text is transformed to a dialogue vector. The dialogue text is classified by a classifier, and the input is the dialogue vector. We conducted sufficient experiments on a real‐world dataset, evaluating the performance of the proposed method. The experimental results show that our method outperforms the related methods on a series of evaluation metrics.