论文标题
在以任务为导向的对话框系统中洞悉未识别的用户话语
Gaining Insights into Unrecognized User Utterances in Task-Oriented Dialog Systems
论文作者
论文摘要
市场对能够以目标为导向行为的自动对话代理的市场需求迅速增长，导致许多技术行业领导者投入大量努力，以将其投入到以任务为导向的对话系统上。这些系统的成功在很大程度上取决于其意图识别的准确性 - 推论用户请求的目标或含义并将其映射到已知的进一步处理意图之一的过程。因此，洞悉无法识别的话语 - 用户要求系统无法归因于已知意图 - 因此是不断改进目标对话框系统的关键过程。 我们提出了一种用于处理未识别的用户话语的端到端管道，该管道部署在现实世界中，以任务为导向的商业对话框系统中，包括一种特殊销量的聚类算法，一种新颖的方法，用于集群代表性提取和群集名称。我们评估了提出的组件，并在分析未认可的用户请求时证明了它们的好处。
The rapidly growing market demand for automatic dialogue agents capable of goal-oriented behavior has caused many tech-industry leaders to invest considerable efforts into task-oriented dialog systems. The success of these systems is highly dependent on the accuracy of their intent identification -- the process of deducing the goal or meaning of the user's request and mapping it to one of the known intents for further processing. Gaining insights into unrecognized utterances -- user requests the systems fail to attribute to a known intent -- is therefore a key process in continuous improvement of goal-oriented dialog systems. We present an end-to-end pipeline for processing unrecognized user utterances, deployed in a real-world, commercial task-oriented dialog system, including a specifically-tailored clustering algorithm, a novel approach to cluster representative extraction, and cluster naming. We evaluated the proposed components, demonstrating their benefits in the analysis of unrecognized user requests.
