ABSTRACT
Product pages on e-commerce websites often overwhelm their customers with a wealth of data, making discovery of relevant information a challenge. Motivated by this, here, we present a novel framework to answer both factoid and non-factoid user questions on product pages. We propose several question-answer matching models leveraging both deep learned distributional semantics and semantics imposed by a structured resource like a domain specific ontology. The proposed framework supports the use of a combination of these models and we show, through empirical evaluation, that a cascade of these models does much better in meeting the high precision requirements of such a question-answering system. Evaluation on user asked questions shows that the proposed system achieves 66% higher precision1 as compared to IDF-weighted average of word vectors baseline [1].
- S. Arora, Y. Liang, and T. Ma. 2016. A Simple but Tough-to-Beat Baseline for Sentence Embeddings. (2016).Google Scholar
- P. Bojanowski, E. Grave, A. Joulin, and T. Mikolov. 2016. Enriching Word Vectors with Subword Information. CoRR (2016).Google Scholar
- Lei Cui, Furu Wei, Shaohan Huang, Chuanqi Tan, Chaoqun Duan, and Ming Zhou. 2017. SuperAgent: A Customer Service Chatbot for E-commerce Websites, In Proceedings of ACL 2017, System Demonstrations. 97–102.Google ScholarCross Ref
- Y. LeCun E. Sackinger J. Bromley, I. Guyonand R. Shah. 1994. Signature Verification using a Siamese Time Delay Neural Network. In NIPS.Google Scholar
- A. Joulin, E. Grave, P. Bojanowski, and T. Mikolov. 2016. Bag of Tricks for Efficient Text Classification. arXiv preprint arXiv:1607.01759(2016).Google Scholar
- Y. Kim. 2014. Convolutional Neural Networks for Sentence Classification. arXiv preprint arXiv:1408.5882(2014).Google Scholar
- S. Lee and Y. Hu. 2015. Joint Embedding of Query and Ad by Leveraging Implicit Feedback. In EMNLP.Google Scholar
- X. Li. 2003. Syntactic Features in Question Answering. In Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval. Google ScholarDigital Library
- B. Xiang M. Tan, C. Santos and B. Zhou. 2015. LSTM-based Deep Learning Models for Non-Factoid Answer Selection. arXiv preprint arXiv:1511.04108(2015).Google Scholar
- T. Mikolov, K. Chen, G. Corrado, and J. Dean. 2013. Efficient Estimation of Word Representations in Vector Space. arXiv preprint arXiv:1301.3781(2013).Google ScholarDigital Library
- T. Nguyen, M. Rosenberg, X. Song, J. Gao, S. Tiwary, R. Majumder, and L. Deng. 2016. MS MARCO: A Human Generated Machine Reading Comprehension Dataset. arXiv preprint arXiv:1611.09268(2016).Google Scholar
- F. Peng, R. Weischedel, A. Licuanan, and J. Xu. 2005. Combining Deep Linguistics Analysis and Surface Pattern Learning: A Hybrid Approach to Chinese Definitional Question Answering. In HLT/EMNLP. Google ScholarDigital Library
- P. Rajpurkar, J. Zhang, K. Lopyrev, and P. Liang. 2016. SQuAD: 100,000+ Questions for Machine Comprehension of Text. CoRR (2016).Google Scholar
- D. Ravichandran and E. Hovy. 2002. Learning Surface Text Patterns for a Question Answering System. In ACL. Google ScholarDigital Library
- D. Ravichandran, A. Ittycheriah, and S. Roukos. 2003. Automatic Derivation of Surface Text Patterns for a Maximum Entropy based Question Answering System. In HLT-NAACL–Short Papers. Google ScholarDigital Library
- C. N. Santos, B. Xiang, and B. Zhou. 2015. Classifying Relations by Ranking with Convolutional Neural Networks. arXiv preprint arXiv:1504.06580(2015).Google Scholar
- R. Soricut and E. Brill. 2006. Automatic Question Answering using The Web: Beyond the Factoid. Information Retrieval(2006). Google ScholarDigital Library
- E. M. Voorhees. 2001. The TREC Question Answering Track. Natural Language Engineering(2001). Google ScholarDigital Library
- D. Wang and E. Nyberg. 2015. A Long Short-Term Memory Model for Answer Sentence Selection in Question Answering. In ACL-IJCNLP (Volume 2: Short Papers).Google Scholar
- M. Wang. 2006. A Survey of Answer Extraction Techniques in Factoid Question Answering. Computational Linguistics(2006).Google Scholar
- J. Wieting, M. Bansal, K. Gimpel, and K. Livescu. 2015. Towards Universal Paraphrastic Sentence Embeddings. arXiv preprint arXiv:1511.08198(2015).Google Scholar
- Z. Yan, N. Duan, P. Chen, M. Zhou, J. Zhou, and Z. Li. 2017. Building Task-Oriented Dialogue Systems for Online Shopping. In AAAI. Google ScholarDigital Library
Index Terms
- ProductQnA: Answering User Questions on E-Commerce Product Pages
Recommendations
Opinion-aware Answer Generation for Review-driven Question Answering in E-Commerce
CIKM '20: Proceedings of the 29th ACM International Conference on Information & Knowledge ManagementProduct-related question answering (QA) is an important but challenging task in E-Commerce. It leads to a great demand on automatic review-driven QA, which aims at providing instant responses towards user-posted questions based on diverse product ...
Answer Ranking for Product-Related Questions via Multiple Semantic Relations Modeling
SIGIR '20: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information RetrievalMany E-commerce sites now offer product-specific question answering platforms for users to communicate with each other by posting and answering questions during online shopping. However, the multiple answers provided by ordinary users usually vary ...
Review-Driven Answer Generation for Product-Related Questions in E-Commerce
WSDM '19: Proceedings of the Twelfth ACM International Conference on Web Search and Data MiningThe users often have many product-related questions before they make a purchase decision in E-commerce. However, it is often time-consuming to examine each user review to identify the desired information. In this paper, we propose a novel review-driven ...
Comments