With the development of dialog techniques, conversational search has attracted more and more attention as it enables users to interact with the search engine in a natural and efficient manner. However, comparing with the natural language understanding in traditional task-oriented dialog which focuses on slot filling and tracking, the query understanding in E-commerce conversational search is quite different and more challenging due to more diverse user expressions and complex intentions. In this work, we define the real-world problem of query tracking in E-commerce conversational search, in which the goal is to update the internal query after each round of interaction. We also propose a self attention based neural network to handle the task in a machine comprehension perspective. Further more we build a novel E-commerce query tracking dataset from an operational E-commerce Search Engine, and experimental results on this dataset suggest that our proposed model outperforms several baseline methods by a substantial gain for Exact Match accuracy and F1 score, showing the potential of machine comprehension like model for this task.

本文提出了利用基于自我注意力的神经网络的机器理解方法，来解决电子商务对话搜索中的查询跟踪问题，并构建了一种新的数据集，实验结果表明，该模型在精确匹配度和F1得分方面优于几种基线模型，展示了机器理解模型在此任务上的潜力。

电子商务对话搜索的查询跟踪：基于机器理解的视角