  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.
