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.
- Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014. Neural machine translation by jointly learning to align and translate. arXiv (2014).Google Scholar
- Vineet Kumar and Sachindra Joshi. 2017. Incomplete Follow-up Question Resolution using Retrieval based Sequence to Sequence Learning. In SIGIR . Google ScholarDigital Library
- Jiwei Li, Michel Galley, Chris Brockett, Georgios Spithourakis, Jianfeng Gao, and Bill Dolan. 2016. A Persona-Based Neural Conversation Model. In ACL .Google Scholar
- Xiujun Li, Yun-Nung Chen, Lihong Li, Jianfeng Gao, and Asli Celikyilmaz. 2017. End-to-End Task-Completion Neural Dialogue Systems. In IJCNLP .Google Scholar
- Lili Mou, Rui Men, Ge Li, Yan Xu, Lu Zhang, Rui Yan, and Zhi Jin. 2016. Natural Language Inference by Tree-Based Convolution and Heuristic Matching. In ACL .Google Scholar
- Filip Radlinski and Nick Craswell. 2017. A theoretical framework for conversational search. In CHIIR . Google ScholarDigital Library
- Gary Ren, Xiaochuan Ni, Manish Malik, and Qifa Ke. 2018. Conversational Query Understanding Using Sequence to Sequence Modeling. In WWW . Google ScholarDigital Library
- Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In NIPS .Google Scholar
- Beidou Wang, Martin Ester, Jiajun Bu, Yu Zhu, Ziyu Guan, and Deng Cai. 2016. Which to view: Personalized prioritization for broadcast emails. In WWW . Google ScholarDigital Library
- Shuohang Wang and Jing Jiang. 2016. Machine comprehension using match-lstm and answer pointer. arXiv (2016).Google Scholar
- Zhao Yan, Nan Duan, Peng Chen, Ming Zhou, Jianshe Zhou, and Zhoujun Li. 2017. Building Task-Oriented Dialogue Systems for Online Shopping.. In AAAI .Google Scholar
- Yu Zhu, Hao Li, Yikang Liao, Beidou Wang, Ziyu Guan, Haifeng Liu, and Deng Cai. 2017. What to do next: modeling user behaviors by time-LSTM. In AAAI . Google ScholarDigital Library
Index Terms
- Query Tracking for E-commerce Conversational Search: A Machine Comprehension Perspective
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