Query Tracking for E-commerce Conversational Search: A Machine Comprehension Perspective
This addresses the challenge of diverse user expressions and complex intentions in E-commerce search, offering an incremental improvement for enhancing conversational search engines.
The paper tackles the problem of query tracking in E-commerce conversational search by defining it as updating internal queries after interactions and proposes a self-attention neural network from a machine comprehension perspective. Experimental results on a novel dataset show the model outperforms baselines with substantial gains in Exact Match accuracy and F1 score.
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.