论文标题
对话框ACT分类的普遍性个人集成模型
A Universality-Individuality Integration Model for Dialog Act Classification
论文作者
论文摘要
对话法（DA）在对话中揭示了说话者话语的总体意图。准确地预测DA可以极大地促进对话代理的开发。尽管研究人员对对话行为进行了广泛的研究，但分类的特征信息尚未得到充分考虑。本文建议，词提示，言论提示和统计提示可以相互补充以改善识别的基础。此外，这三种类型的不同类型导致其分布形式的多样性，这阻碍了特征信息的采矿。为了解决这个问题，我们提出了一个基于普遍性和个性策略的新型模型，称为普遍性 - 个性集成模型（UIIM）。 UIIM不仅通过学习通用性来加深线索之间的联系，而且还利用个性学习来捕获线索本身的特征。实验是在两个最受欢迎的基准数据集SWDA和MRDA进行对话ACT分类的情况下进行的，结果表明，在提示之间提取普遍性和个性可以更充分地挖掘出讲话中的隐藏信息，并提高自动对话ACT识别的准确性。
Dialog Act (DA) reveals the general intent of the speaker utterance in a conversation. Accurately predicting DAs can greatly facilitate the development of dialog agents. Although researchers have done extensive research on dialog act classification, the feature information of classification has not been fully considered. This paper suggests that word cues, part-of-speech cues and statistical cues can complement each other to improve the basis for recognition. In addition, the different types of the three lead to the diversity of their distribution forms, which hinders the mining of feature information. To solve this problem, we propose a novel model based on universality and individuality strategies, called Universality-Individuality Integration Model (UIIM). UIIM not only deepens the connection between the clues by learning universality, but also utilizes the learning of individuality to capture the characteristics of the clues themselves. Experiments were made over two most popular benchmark data sets SwDA and MRDA for dialogue act classification, and the results show that extracting the universalities and individualities between cues can more fully excavate the hidden information in the utterance, and improve the accuracy of automatic dialogue act recognition.
