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Lessons from Building a Large-scale Commercial IR-based Chatbot for an Emerging Market

Published: 27 June 2018 Publication History

Abstract

In this work, we highlight some interesting challenges faced when trying to build a large-scale commercial IR-based chatbot, Ruuh, for an emerging market like India which has unique characteristics such as high linguistic and cultural diversity, large section of young population and the second largest mobile market in the world. We set out to build a "human-like" AI agent which aspires to become the trusted friend of every Indian youth. To meet this objective, we realised that we need to think beyond the utilitarian notion of merely generating "relevant" responses and enable the agent to comprehend and meet a wider range of user social needs, like expressing happiness when user's favourite team wins, sharing a cute comment on showing the pictures of the user's pet and so on. The agent should also be well-versed with the informal language of the urban Indian youth which often includes slang and code-mixing across two or more languages (English and their native language). Finally, in order to be their trusted friend, the agent has to communicate with respect without offending their sentiments and emotions. Some of the above objectives pose significant research challenges in the areas of NLP, IR and AI. We take the audience through our journey of how we tackled some of the above challenges while building a large-scale commercial IR-based conversational agent. Our attempts to solve some of the above challenges have also resulted in some interesting research contributions in the form of publications and patents in the above areas. Our chat-bot currently has more than 1M users who have engaged in more than 70M conversations.

References

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Khyathi Raghavi Chandu, Manoj Chinnakotla, Alan W. Black, and Manish Shrivastava . 2017. WebShodh: A Code Mixed Factoid Question Answering System for Web Proceedings of CLEF 2017. Springer, 104--111.
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Hao Fang, Saurabh Gupta, Forrest Iandola, Rupesh K Srivastava, Li Deng, Piotr Dollár, Jianfeng Gao, Xiaodong He, Margaret Mitchell, John C Platt, et almbox. . 2015. From captions to visual concepts and back. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 1473--1482.
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Umang Gupta, Ankush Chatterjee, Radhakrishnan Srikanth, and Puneet Agrawal . 2017. A Sentiment-and-Semantics-Based Approach for Emotion Detection in Textual Conversations Neu-IR: The SIGIR 2017 Workshop on Neural Information Retrieval.
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Abhay Prakash, Chris Brockett, and Puneet Agrawal . 2016. Emulating human conversations using convolutional neural network-based ir. Neu-IR: The SIGIR 2016 Workshop on Neural Information Retrieval.
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Harish Yenala, Manoj Chinnakotla, and Jay Goyal . 2017 a. Convolutional Bidirectional LSTM for Detecting Inappropriate Query Suggestions in Web Search Proceedings of PAKDD 2017. 3--16.
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Cited By

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  • (2024)Transparent, Low Resource, and Context-Aware Information Retrieval From a Closed Domain Knowledge BaseIEEE Access10.1109/ACCESS.2024.338000612(44233-44243)Online publication date: 2024
  • (2022)Information Need AwarenessProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3531999(610-621)Online publication date: 6-Jul-2022
  • (2019)A Short Survey on Chatbot Technology: Failure in Raising the State of the ArtDistributed Computing and Artificial Intelligence, 16th International Conference10.1007/978-3-030-23887-2_4(28-36)Online publication date: 22-Jun-2019

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cover image ACM Conferences
SIGIR '18: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval
June 2018
1509 pages
ISBN:9781450356572
DOI:10.1145/3209978
Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 June 2018

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Author Tags

  1. chatbot
  2. codemix
  3. emotion
  4. image
  5. offensive

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SIGIR '18
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SIGIR '18 Paper Acceptance Rate 86 of 409 submissions, 21%;
Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

View all
  • (2024)Transparent, Low Resource, and Context-Aware Information Retrieval From a Closed Domain Knowledge BaseIEEE Access10.1109/ACCESS.2024.338000612(44233-44243)Online publication date: 2024
  • (2022)Information Need AwarenessProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3531999(610-621)Online publication date: 6-Jul-2022
  • (2019)A Short Survey on Chatbot Technology: Failure in Raising the State of the ArtDistributed Computing and Artificial Intelligence, 16th International Conference10.1007/978-3-030-23887-2_4(28-36)Online publication date: 22-Jun-2019

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