short-paper

Query-bag Matching with Mutual Coverage for Information-seeking Conversations in E-commerce

Authors:

Zhenxin Fu,

Feng Ji,

Wenpeng Hu,

Wei Zhou, Dongyan Zhao,

Haiqing Chen,

Rui YanAuthors Info & Claims

CIKM '19: Proceedings of the 28th ACM International Conference on Information and Knowledge Management

Pages 2337 - 2340

https://doi.org/10.1145/3357384.3358099

Published: 03 November 2019 Publication History

Get Access

Abstract

Information-seeking conversation system aims at satisfying the information needs of users through conversations. Text matching between a user query and a pre-collected question is an important part of the information-seeking conversation in E-commerce. In the practical scenario, a sort of questions always correspond to a same answer. Naturally, these questions can form a bag. Learning the matching between user query and bag directly may improve the conversation performance, denoted as query-bag matching. Inspired by such opinion, we propose a query-bag matching model which mainly utilizes the mutual coverage between query and bag and measures the degree of the content in the query mentioned by the bag, and vice verse. In addition, the learned bag representation in word level helps find the main points of a bag in a fine grade and promotes the query-bag matching performance. Experiments on two datasets show the effectiveness of our model.

References

[1]

Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015. Neural machine translation by jointly learning to align and translate. ICLR (2015).

Google Scholar

[2]

Qian Chen, Xiaodan Zhu, Zhen-Hua Ling, Si Wei, Hui Jiang, and Diana Inkpen. 2017. Enhanced LSTM for Natural Language Inference. In ACL. 1657--1668.

Google Scholar

[3]

Hang Li and Jun Xu. 2012. Machine Learning for Query-Document Matching in Web Search. (2012).

Google Scholar

[4]

Minghui Qiu, Liu Yang, Feng Ji, Wei Zhou, Jun Huang, Haiqing Chen, Bruce Croft, and Wei Lin. 2018. Transfer Learning for Context-Aware Question Matching in Information-seeking Conversations in E-commerce. In ACL. 208--213.

Google Scholar

[5]

Gaurav Singh Tomar, Thyago Duque, Oscar T"ackström, Jakob Uszkoreit, and Dipanjan Das. 2017. Neural Paraphrase Identification of Questions with Noisy Pretraining. In Proceedings of the First Workshop on Subword and Character Level Models in NLP . Association for Computational Linguistics, 142--147.

Crossref

Google Scholar

[6]

Yu Wu, Wei Wu, Chen Xing, Ming Zhou, and Zhoujun Li. 2017. Sequential Matching Network: A New Architecture for Multi-turn Response Selection in Retrieval-Based Chatbots. In ACL. 496--505.

Google Scholar

[7]

Runqi Yang, Jianhai Zhang, Xing Gao, Feng Ji, and Haiqing Chen. 2019. Simple and Effective Text Matching with Richer Alignment Features. In ACL . 4699--4709.

Google Scholar

[8]

Jianfei Yu, Minghui Qiu, Jing Jiang, Jun Huang, Shuangyong Song, Wei Chu, and Haiqing Chen. 2018. Modelling domain relationships for transfer learning on retrieval-based question answering systems in e-commerce. In WSDM. 682--690.

Google Scholar

Cited By

View all

Tleuken AOrel DIskakova AVarol HKaraca F(2024)Exploring stakeholders’ opinions on circular economy in the construction sector: A natural language processing analysis of social media discourseJournal of Industrial Ecology10.1111/jiec.1350228:4(853-867)Online publication date: 17-Jun-2024
https://doi.org/10.1111/jiec.13502

Index Terms

Query-bag Matching with Mutual Coverage for Information-seeking Conversations in E-commerce
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Discourse, dialogue and pragmatics
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Business intelligence
      2. Question answering

Recommendations

Bag Query Containment and Information Theory
PODS'20: Proceedings of the 39th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems

The query containment problem is a fundamental algorithmic problem in data management. While this problem is well understood under set semantics, it is by far less understood under bag semantics. In particular, it is a long-standing open question ...
Bag Query Containment and Information Theory
The query containment problem is a fundamental algorithmic problem in data management. While this problem is well understood under set semantics, it is by far less understood under bag semantics. In particular, it is a long-standing open question whether ...
Multiple instance learning with bag dissimilarities

Multiple instance learning (MIL) is concerned with learning from sets (bags) of objects (instances), where the individual instance labels are ambiguous. In this setting, supervised learning cannot be applied directly. Often, specialized MIL methods ...

Comments

Information & Contributors

Information

Published In

CIKM '19: Proceedings of the 28th ACM International Conference on Information and Knowledge Management

November 2019

3373 pages

ISBN:9781450369763

DOI:10.1145/3357384

General Chairs:
Wenwu Zhu
Tsinghua University, China
,
Dacheng Tao
University of Massachusetts, USA
,
Xueqi Cheng
Institute of Computing Technology, CAS, China
,
Program Chairs:
Peng Cui
Tsinghua University, China
,
Elke Rundensteiner
Worcester Polytechnic Institute, USA
,
David Carmel
Amazon Research, USA
,
Qi He
LinkedIn, USA
,
Jeffrey Xu Yu
Chinese University of Hong Kong, China

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 03 November 2019

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Short-paper

Funding Sources

National Key R&D Program of China
National Science Foundation of China

Conference

CIKM '19

Sponsor:

CIKM '19: The 28th ACM International Conference on Information and Knowledge Management

November 3 - 7, 2019

Beijing, China

Acceptance Rates

CIKM '19 Paper Acceptance Rate 202 of 1,031 submissions, 20%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

Upcoming Conference

CIKM '25

Sponsor:
sigir
sigir

The 34th ACM International Conference on Information and Knowledge Management

November 10 - 14, 2025

Seoul , Republic of Korea

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

1
Total Citations
View Citations
132
Total Downloads

Downloads (Last 12 months)3
Downloads (Last 6 weeks)0

Reflects downloads up to 09 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Tleuken AOrel DIskakova AVarol HKaraca F(2024)Exploring stakeholders’ opinions on circular economy in the construction sector: A natural language processing analysis of social media discourseJournal of Industrial Ecology10.1111/jiec.1350228:4(853-867)Online publication date: 17-Jun-2024
https://doi.org/10.1111/jiec.13502

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

Bag Query Containment and Information Theory

Bag Query Containment and Information Theory

Multiple instance learning with bag dissimilarities