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DuIVRS: A Telephonic Interactive Voice Response System for Large-Scale POI Attribute Acquisition at Baidu Maps

Published: 17 October 2022 Publication History

Abstract

The task of POI attribute acquisition, which aims at completing missing attributes (e.g., POI name, address, status, phone, and open/close time) for a point of interest (POI) or updating existing attribute values of a POI, plays an essential role in enabling users to entertain location-based services using commercial map applications, such as Baidu Maps. Existing solutions have adopted street views or web documents to acquire POI attributes, which have a major limitation in applying for large-scale production due to the labor-intensive and time-consuming nature of collecting data, error accumulation in processing textual/visual data in unstructured or free format, and necessitating post-processing steps with manual efforts. In this paper, we present our efforts and findings from a 3-year longitudinal study on designing and implementing DuIVRS, which is an alternative, fully automatic, and production-proven solution for large-scale POI attribute acquisition via completely machine-directed dialogues. Specifically, DuIVRS is designed to proactively acquire POI attributes via a telephonic interactive voice response system, whose tasks are to generate machine-initiative directed dialogues, make scripted telephone calls to businesses, and interact with people who answered the phone to achieve predefined goals through multi-turn dialogues. DuIVRS has already been deployed in production at Baidu Maps since December 2018, which greatly improves productivity and reduces production cost of POI attribute acquisition. As of December 31, 2021, DuIVRS has made 140 million calls and 42 million POI attribute updates within a 3-year period, which represents an approximately 3-year workload for a high-performance team of 1,000 call center workers. This demonstrates that DuIVRS is an industrial-grade and robust solution for cost-effective, large-scale acquisition of POI attributes.

References

[1]
Pranav Bhagat, Sachin Kumar Prajapati, and Aaditeshwar Seth. 2020. Initial Lessons from Building an IVR-Based Automated Question-Answering System. In Proceedings of the 2020 International Conference on Information and Communication Technologies and Development. Article 27, 5 pages.
[2]
Nathanael Chambers and Dan Jurafsky. 2011. Template-based information extraction without the templates. In Proceedings of the 49th annual meeting of the association for computational linguistics: human language technologies. 976--986.
[3]
Hongshen Chen, Xiaorui Liu, Dawei Yin, and Jiliang Tang. 2017. A Survey on Dialogue Systems: Recent Advances and New Frontiers. ACM SIGKDD Explorations Newsletter, Vol. 19, 2 (2017), 25--35.
[4]
Yudong Chen, Xin Wang, Miao Fan, Jizhou Huang, Shengwen Yang, and Wenwu Zhu. 2021. Curriculum meta-learning for next POI recommendation. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. 2692--2702.
[5]
Hsiu-Min Chuang and Chia-Hui Chang. 2015. Verification of POI and Location Pairs via Weakly Labeled Web Data. In Proceedings of the 24th International Conference on World Wide Web. 743--748.
[6]
Don A Dillman, Glenn Phelps, Robert Tortora, Karen Swift, Julie Kohrell, Jodi Berck, and Benjamin L Messer. 2009. Response rate and measurement differences in mixed-mode surveys using mail, telephone, interactive voice response (IVR) and the Internet. Social science research, Vol. 38, 1 (2009), 1--18.
[7]
Miao Fan, Jizhou Huang, and Haifeng Wang. 2022. DuMapper: Towards Automatic Verification of Large-Scale POIs with Street Views at Baidu Maps. In Proceedings of the 31st ACM International Conference on Information and Knowledge Management.
[8]
Miao Fan, Yibo Sun, Jizhou Huang, Haifeng Wang, and Ying Li. 2021. Meta-Learned Spatial-Temporal POI Auto-Completion for the Search Engine at Baidu Maps. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. 2822--2830.
[9]
Li Gong and Jennifer Lai. 2003. To Mix or Not to Mix Synthetic Speech and Human Speech? Contrasting Impact on Judge-Rated Task Performance versus Self-Rated Performance and Attitudinal Responses. International Journal of Speech Technology, Vol. 6, 2 (2003), 123--131.
[10]
Donghoon Ham, J-G Lee, Youngsoo Jang, and K-E Kim. 2020. End-to-end neural pipeline for goal-oriented dialogue systems using GPT-2. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 583--592.
[11]
Jizhou Huang, Haifeng Wang, Shiqiang Ding, and Shaolei Wang. 2022a. DuIVA: An Intelligent Voice Assistant for Hands-free and Eyes-free Voice Interaction with the Baidu Maps App. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 3040--3050.
[12]
Jizhou Huang, Haifeng Wang, Miao Fan, An Zhuo, and Ying Li. 2020a. Personalized Prefix Embedding for POI Auto-Completion in the Search Engine of Baidu Maps. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2677--2685.
[13]
Jizhou Huang, Haifeng Wang, Miao Fan, An Zhuo, Yibo Sun, and Ying Li. 2020b. Understanding the Impact of the COVID-19 Pandemic on Transportation-Related Behaviors with Human Mobility Data. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 3443--3450.
[14]
Jizhou Huang, Haifeng Wang, Yibo Sun, Miao Fan, Zhengjie Huang, Chunyuan Yuan, and Yawen Li. 2021. HGAMN: Heterogeneous Graph Attention Matching Network for Multilingual POI Retrieval at Baidu Maps. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. 3032--3040.
[15]
Jizhou Huang, Haifeng Wang, Yibo Sun, Yunsheng Shi, Zhengjie Huang, An Zhuo, and Shikun Feng. 2022b. ERNIE-GeoL: A Geography-and-Language Pre-Trained Model and Its Applications in Baidu Maps. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 3029--3039.
[16]
Jizhou Huang, Ming Zhou, and Dan Yang. 2007. Extracting Chatbot Knowledge from Online Discussion Forums. In Proceedings of the Twentieth International Joint Conference on Artificial Intelligence. 423--428.
[17]
N Kaji, M Okamoto, and S Kurohashi. 2004. Paraphrasing Predicates from Written Language to Spoken Language Using the Web. In Proceedings of the Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics: HLT-NAACL 2004. 241--248.
[18]
Chin-Hui Lee, Bob Carpenter, Wu Chou, Jennifer Chu-Carroll, Wolfgang Reichl, Antoine Saad, and Qiru Zhou. 2000. On natural language call routing. Speech Communication, Vol. 31, 4 (2000), 309--320.
[19]
Wenqiang Lei, Xisen Jin, Min-Yen Kan, Zhaochun Ren, Xiangnan He, and Dawei Yin. 2018. Sequicity: Simplifying task-oriented dialogue systems with single sequence-to-sequence architectures. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 1437--1447.
[20]
Yaniv Leviathan and Yossi Matias. 2018. Google Duplex: An AI system for accomplishing real-world tasks over the phone. Google AI blog.
[21]
Olivier Pietquin and Thierry Dutoit. 2006. A Probabilistic Framework for Dialog Simulation and Optimal Strategy Learning. IEEE Transactions on Audio, Speech, and Language Processing, Vol. 14, 2 (2006), 589--599.
[22]
Adam Rae, Vanessa Murdock, Adrian Popescu, and Hugues Bouchard. 2012. Mining the Web for Points of Interest. In The 35th International ACM SIGIR conference on research and development in Information Retrieval. 711--720.
[23]
Jé rô me Revaud, Matthijs Douze, and Cordelia Schmid. 2012. Correlation-Based Burstiness for Logo Retrieval. In Proceedings of the 20th ACM Multimedia Conference. 965--968.
[24]
Hang Su, Shaogang Gong, and Xiatian Zhu. 2017. WebLogo-2M: Scalable Logo Detection by Deep Learning from the Web. In 2017 IEEE International Conference on Computer Vision Workshops. 270--279.
[25]
B Suhm and P Peterson. 2002. A data-driven methodology for evaluating and optimizing call center IVRs. Internat. J. of Speech Technology, Vol. 5, 1 (2002), 23--37.
[26]
Yibo Sun, Jizhou Huang, Chunyuan Yuan, Miao Fan, Haifeng Wang, Ming Liu, and Bing Qin. 2021. GEDIT: Geographic-Enhanced and Dependency-Guided Tagging for Joint POI and Accessibility Extraction at Baidu Maps. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management. 4135--4144.
[27]
Chris van der Lee, Emiel Krahmer, and Sander Wubben. 2018. Automated learning of templates for data-to-text generation: comparing rule-based, statistical and neural methods. In Proceedings of the 11th International Conference on Natural Language Generation. 35--45.
[28]
Tsung-Hsien Wen, David Vandyke, Nikola Mrkvs ić, Milica Gasic, Lina M Rojas Barahona, Pei-Hao Su, Stefan Ultes, and Steve Young. 2017. A Network-based End-to-End Trainable Task-oriented Dialogue System. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers. 438--449.
[29]
Michael Witbrock, David Baxter, Jon Curtis, Dave Schneider, Robert Kahlert, Pierluigi Miraglia, Peter Wagner, Kathy Panton, Gavin Matthews, and Amanda Vizedom. 2003. An Interactive Dialogue System for Knowledge Acquisition in Cyc. In Proceedings of the 18th International Joint Conference on Artificial Intelligence. 138--145.
[30]
Congxi Xiao, Jingbo Zhou, Jizhou Huang, An Zhuo, Ji Liu, Haoyi Xiong, and Dejing Dou. 2021. C-watcher: A framework for early detection of high-risk neighborhoods ahead of COVID-19 outbreak. In Proceedings of the AAAI Conference on Artificial Intelligence. 4892--4900.
[31]
Canwen Xu, Jing Li, Xiangyang Luo, Jiaxin Pei, Chenliang Li, and Donghong Ji. 2019. DLocRL: A Deep Learning Pipeline for Fine-Grained Location Recognition and Linking in Tweets. In The World Wide Web Conference. 3391--3397.
[32]
Zhao Yan, Nan Duan, Peng Chen, Ming Zhou, Jianshe Zhou, and Zhoujun Li. 2017. Building Task-Oriented Dialogue Systems for Online Shopping. In Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence. 4618--4625.
[33]
Xuejie Zhang, Samarth Agarwal, Ruth Choy, Kay Jan Wong, Lecia Lim, Ying Yang Lee, and John Jianan Lu. 2020. Personalized Digital Customer Services for Consumer Banking Call Centre using Neural Networks. In 2020 International Joint Conference on Neural Networks (IJCNN). 1--7.

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  • (2022)DuMapper: Towards Automatic Verification of Large-Scale POIs with Street Views at Baidu MapsProceedings of the 31st ACM International Conference on Information & Knowledge Management10.1145/3511808.3557097(3063-3071)Online publication date: 17-Oct-2022

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cover image ACM Conferences
CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management
October 2022
5274 pages
ISBN:9781450392365
DOI:10.1145/3511808
  • General Chairs:
  • Mohammad Al Hasan,
  • Li Xiong
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 ACM 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]

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Published: 17 October 2022

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Author Tags

  1. Baidu maps
  2. POI attribute acquisition
  3. interactive voice response system
  4. knowledge acquisition
  5. task oriented dialogue system

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CIKM '22 Paper Acceptance Rate 621 of 2,257 submissions, 28%;
Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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  • (2023)AutoBuild: Automatic Community Building Labeling for Last-mile DeliveryProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614658(4623-4630)Online publication date: 21-Oct-2023
  • (2022)DuMapper: Towards Automatic Verification of Large-Scale POIs with Street Views at Baidu MapsProceedings of the 31st ACM International Conference on Information & Knowledge Management10.1145/3511808.3557097(3063-3071)Online publication date: 17-Oct-2022

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