论文标题
从过去的对话对话代理的对话中挖掘的意图
Intent Mining from past conversations for conversational agent
论文作者
论文摘要
会话系统是AI社区的主要兴趣。聊天机器人越来越多地部署以提供全天候的支持并增加客户参与度。许多商业机器人构建框架都遵循一种标准方法，该方法需要一个人来构建和培训意图模型以识别用户输入。意图模型在有监督的环境中培训，并收集了一系列文字话语和意图标签对。在机器人构建过程中，收集大量培训数据的大量培训数据是一种瓶颈。此外，用意图进行一百千万对话标记的成本是一项耗时且费力的工作。 In this paper, we present an intent discovery framework that involves 4 primary steps: Extraction of textual utterances from a conversation using a pre-trained domain agnostic Dialog Act Classifier (Data Extraction), automatic clustering of similar user utterances (Clustering), manual annotation of clusters with an intent label (Labeling) and propagation of intent labels to the utterances from the previous step, which are not mapped to any cluster （标签传播）；从原始对话中生成意图培训数据。我们引入了一种新型的基于密度的聚类算法ITER-DBSCAN，以实现不平衡的数据聚类。主题专家（具有域专业知识的注释者）手动着眼于群集用户的话语，并为发现提供了意图标签。我们进行了用户研究，以验证培训意图，意图，准确性和节省时间有关的手动注释的有效性。尽管该系统是用于为对话系统构建意图模型的开发，但该框架也可以用于简短的文本聚类或标签框架。
Conversational systems are of primary interest in the AI community. Chatbots are increasingly being deployed to provide round-the-clock support and to increase customer engagement. Many of the commercial bot building frameworks follow a standard approach that requires one to build and train an intent model to recognize a user input. Intent models are trained in a supervised setting with a collection of textual utterance and intent label pairs. Gathering a substantial and wide coverage of training data for different intent is a bottleneck in the bot building process. Moreover, the cost of labeling a hundred to thousands of conversations with intent is a time consuming and laborious job. In this paper, we present an intent discovery framework that involves 4 primary steps: Extraction of textual utterances from a conversation using a pre-trained domain agnostic Dialog Act Classifier (Data Extraction), automatic clustering of similar user utterances (Clustering), manual annotation of clusters with an intent label (Labeling) and propagation of intent labels to the utterances from the previous step, which are not mapped to any cluster (Label Propagation); to generate intent training data from raw conversations. We have introduced a novel density-based clustering algorithm ITER-DBSCAN for unbalanced data clustering. Subject Matter Expert (Annotators with domain expertise) manually looks into the clustered user utterances and provides an intent label for discovery. We conducted user studies to validate the effectiveness of the trained intent model generated in terms of coverage of intents, accuracy and time saving concerning manual annotation. Although the system is developed for building an intent model for the conversational system, this framework can also be used for a short text clustering or as a labeling framework.
