多轮对话意图识别方法的研究与应用

Jie Song, Qifeng Luo, J. Nie
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引用次数: 0

摘要

在现有的对话系统中,有大量的非规范的言语表达形式的句子,这些句子通常是简短而模糊的。通过分析这些句子来识别意图是一项具有挑战性的任务。考虑到监督学习方法是多意图识别的主流,需要大量公开标注的多意图对话数据。然而,标签工作既昂贵又耗时。本文在现有主流分类算法的基础上,提出了一种多标签分类方法,并将其用于对话级多意图识别,以降低标注工作的成本。我们发布了运输客户服务中文多意向对话(CMID-Transportation)数据集,该数据集是我们在实际生产项目中收集的。我们使用主流分类算法在CMID-Transportation语料库上进行了一系列的实验,然后得出了基本的基准性能。我们发现BERT达到了最好的效果。我们希望CMID-Transportation数据集能够在多轮对话中促进意图识别任务的研究和开发。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research and Application of Multi-Round Dialogue Intent Recognition Method
In the existing dialogue system, there are numerous sentences in non-standardized verbal expression form, which usually is brief and vague. It is a challenging task to identify the intentions through the analysis of these sentences. Considering that the supervised learning approach is the mainstream on multi-intention recognition, an amount of public labeled multi-intention dialogue data is necessary. However, labeling work is costly and time-consuming. In this paper, we put forward a multi-label classification method based on existing mainstream classification algorithms and used for dialogue-level multi-intention recognition to reduce the cost of labeling work. We publish the Chinese Multi-Intention Dialogue (CMID-Transportation) dataset of transportation customer service, which is collected by us in an actual production project. We conduct a series of experiments on the CMID-Transportation corpus by using the mainstream classification algorithms and then produce the basic benchmark performance. We find that BERT achieves the best results. We hope that the CMID-Transportation dataset can promote the research and development of intent recognition tasks in multiple rounds of dialogue.
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