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.

中文翻译：

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具有关键句挖掘的多回合对话系统中的意图分类

多轮对话系统已广泛用于电子商务网站和现代信息系统中，大大提高了解决问题的效率，并进一步提高了服务质量。在多轮对话系统中，意图分类问题是一项核心任务，因为客户的意图是后续问题处理的基础。但是，传统的相关方法不适用于多回合对话的分类。由于传统方法无法区分每个句子的重要性，并且无法将文本中的所有句子连接起来，因此很可能会生成预测精度较低的模型。本文提出了一种基于关键句挖掘的多回合对话分类方法。我们设计了一个关键字提取算法，从对话文本中提取关键句子。我们提出一种算法来完成每个句子的权重计算。根据句子权重和句子向量，将对话文本转换为对话向量。对话文本由分类器分类，输入是对话向量。我们在一个真实的数据集上进行了足够的实验，评估了该方法的性能。实验结果表明，在一系列评估指标上，我们的方法优于相关方法。评估所提出方法的性能。实验结果表明，在一系列评估指标上，我们的方法优于相关方法。评估所提出方法的性能。实验结果表明，在一系列评估指标上，我们的方法优于相关方法。