Query Tracking for E-commerce Conversational Search: A Machine Comprehension Perspective

TL;DR

This paper introduces a novel neural network model for query tracking in E-commerce conversational search, addressing the unique challenges of understanding diverse user expressions and complex intentions, and demonstrates its effectiveness on a new dataset.

Contribution

The paper proposes a self-attention based neural network for query tracking in E-commerce search and creates a new dataset for this task, showing improved performance over baselines.

Findings

Model outperforms baseline methods in Exact Match accuracy.

Model achieves higher F1 scores, indicating better query understanding.

Demonstrates the potential of machine comprehension approaches for E-commerce search.

Abstract

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.