skip to main content
10.1145/3477495.3536326acmconferencesArticle/Chapter ViewAbstractPublication PagesirConference Proceedingsconference-collections
short-paper

Flipping the Script: Inverse Information Seeking Dialogues for Market Research

Published: 07 July 2022 Publication History

Abstract

Information retrieval has traditionally been framed in terms of searching and extracting information from mostly static resources. Interactive information retrieval (IIR) has widened the scope, with interactive dialogues largely playing the role of clarifying (i.e., making explicit, and/or refining) the information search space. Informed by market research practices, we seek to reframe IIR as a process of eliciting novel information from human interlocutors, with a chatbot-inspired virtual agent playing the role of an interviewer. This reframing flips conventional IIR into what we call an inverse information seeking dialogue, wherein the virtual agent recurrently extracts information from human utterances and poses questions intended to elicit related information. In this work, we introduce and provide a formal definition of an inverse information seeking agent, outline some of its unique challenges, and propose our novel framework to tackle this problem based on techniques from natural language processing (NLP) and IIR.

References

[1]
Daniel Adiwardana, Minh-Thang Luong, David R. So, Jamie Hall, Noah Fiedel, Romal Thoppilan, Zi Yang, Apoorv Kulshreshtha, Gaurav Nemade, Yifeng Lu, and Quoc V. Le. 2021. Towards a Human-like Open-Domain Chatbot. arXiv preprint arXiv:2001.09977 (2021).
[2]
Abdullah Al Mamun, Muhammad Mohiuddin, Syed Ali Fazal, and Ghazali Bin Ahmad. 2018. Effect of Entrepreneurial and Market Orientation on Consumer Engagement and Performance of Manufacturing SMEs. Management Research Review (2018).
[3]
John Langshaw Austin. 1975. How To Do Things With Words .Oxford University Press.
[4]
Nofar Carmeli, Xiaolan Wang, Yoshihiko Suhara, Stefanos Angelidis, Yuliang Li, Jinfeng Li, and Wang-Chiew Tan. 2021. Constructing Explainable Opinion Graphs from Reviews. In Proceedings of the Web Conference 2021. 3419--3431.
[5]
W. Bruce Croft. 2019. The Importance of Interaction for Information Retrieval. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval. 1--2.
[6]
Pradeep Dasigi, Kyle Lo, Iz Beltagy, Arman Cohan, Noah A. Smith, and Matt Gardner. 2021. A Dataset of Information-Seeking Questions and Answers Anchored in Research Papers. arXiv preprint arXiv:2105.03011 (2021).
[7]
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). Minneapolis, Minnesota, 4171--4186.
[8]
Jens Dörpinghaus, Johannes Darms, and Marc Jacobs. 2018. What Was the Question? A Systematization of Information Retrieval and NLP Problems. In Proceedings of the 2018 Federated Conference on Computer Science and Information Systems (FedCSIS). 471--478.
[9]
Maria Vittoria Franceschelli, Gabriele Santoro, and Elena Candelo. 2018. Business Model Innovation for Sustainability: A Food Start-up Case Study. British Food Journal (2018).
[10]
Mandar Joshi, Danqi Chen, Yinhan Liu, Daniel S. Weld, Luke Zettlemoyer, and Omer Levy. 2020. SpanBERT: Improving Pre-training by Representing and Predicting Spans. Transactions of the Association for Computational Linguistics, Vol. 8 (2020), 64--77.
[11]
Daniel Jurafsky, Rebecca Bates, Noah Coccaro, Rachel Martin, Marie Meteer, Klaus Ries, Elizabeth Shriberg, Andreas Stolcke, Paul Taylor, and Carol Van Ess-Dykema. 1997. Automatic Detection of Discourse Structure for Speech Recognition and Understanding. In Proceedings of the 1997 IEEE Workshop on Automatic Speech Recognition and Understanding. 88--95.
[12]
Masahiro Kaneko, Masato Mita, Shun Kiyono, Jun Suzuki, and Kentaro Inui. 2020. Encoder-Decoder Models Can Benefit From Pre-trained Masked Language Models in Grammatical Error Correction. arXiv preprint arXiv:2005.00987 (2020).
[13]
Hamed Khanpour, Nishitha Guntakandla, and Rodney Nielsen. 2016. Dialogue Act Classification in Domain-Independent Conversations Using a Deep Recurrent Neural Network. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers. 2012--2021.
[14]
Joseph Lark, Emmanuel Morin, and Sebastián Pe na Saldarriaga. 2017. A Comparative Study of Target-based and Entity-based Opinion Extraction. In International Conference on Computational Linguistics and Intelligent Text Processing. Springer, 211--223.
[15]
Yuliang Li, Aaron Xixuan Feng, Jinfeng Li, Saran Mumick, Alon Halevy, Vivian Li, and Wang-Chiew Tan. 2019. Subjective Databases. arXiv preprint arXiv:1902.09661 (2019).
[16]
Yuliang Li, Jinfeng Li, Yoshihiko Suhara, AnHai Doan, and Wang-Chiew Tan. 2020. Deep Entity Matching with Pre-trained Language Models. arXiv preprint arXiv:2004.00584 (2020).
[17]
Kecheng Liu and Weizi Li. 2014. Organisational Semiotics for Business Informatics .Routledge.
[18]
Deon Mai and Wei Emma Zhang. 2020. Aspect Extraction Using Coreference Resolution and Unsupervised Filtering. In Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing: Student Research Workshop. 124--129.
[19]
Rubén Moreno-Bote, Jorge Ram'irez-Ruiz, Jan Drugowitsch, and Benjamin Y. Hayden. 2020. Heuristics and Optimal Solutions to the Breadth--Depth Dilemma. Proceedings of the National Academy of Sciences, Vol. 117, 33 (2020), 19799--19808.
[20]
N Nuruzzaman, Deeksha Singh, and Chinmay Pattnaik. 2019. Competing to be Innovative: Foreign Competition and Imitative Innovation of Emerging Economy Firms. International Business Review, Vol. 28, 5 (2019), 101490.
[21]
Heiko Paulheim. 2017. Knowledge Graph Refinement: A Survey of Approaches and Evaluation Methods. Semantic Web, Vol. 8, 3 (2017), 489--508.
[22]
Dheeraj Rajagopal, Vivek Khetan, Bogdan Sacaleanu, Anatole Gershman, Andrew Fano, and Eduard Hovy. 2021. Cross-Domain Reasoning via Template Filling. arXiv preprint arXiv:2111.00539 (2021).
[23]
Ivan Sekuliç, Mohammad Aliannejadi, and Fabio Crestani. 2021. Towards Facet-Driven Generation of Clarifying Questions for Conversational Search. In Proceedings of the 2021 ACM SIGIR International Conference on Theory of Information Retrieval. 167--175.
[24]
Ming-Hsiang Su, Chung-Hsien Wu, and Liang-Yu Chen. 2019. Attention-Based Response Generation Using Parallel Double Q-learning for Dialog Policy Decision in a Conversational System. IEEE/ACM Transactions on Audio, Speech, and Language Processing, Vol. 28 (2019), 131--143.
[25]
Shiliang Sun, Chen Luo, and Junyu Chen. 2017. A Review of Natural Language Processing Techniques for Opinion Mining Systems. Information Fusion, Vol. 36 (2017), 10--25.
[26]
Vladimir Vlasov, Johannes E. M. Mosig, and Alan Nichol. 2019. Dialogue Transformers. arXiv preprint arXiv:1910.00486 (2019).
[27]
Albert Weichselbraun, Stefan Gindl, Fabian Fischer, Svitlana Vakulenko, and Arno Scharl. 2017. Aspect-based Extraction and Analysis of Affective Knowledge from Social Media Streams. IEEE Intelligent Systems, Vol. 32, 3 (2017), 80--88.
[28]
Hamed Zamani, Susan Dumais, Nick Craswell, Paul Bennett, and Gord Lueck. 2020. Generating Clarifying Questions for Information Retrieval. In Proceedings of The Web Conference 2020. 418--428.

Cited By

View all
  • (2023)Multimodal Dialogue Understanding via Holistic Modeling and Sequence LabelingNatural Language Processing and Chinese Computing10.1007/978-3-031-44699-3_36(399-411)Online publication date: 12-Oct-2023

Index Terms

  1. Flipping the Script: Inverse Information Seeking Dialogues for Market Research

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Conferences
    SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval
    July 2022
    3569 pages
    ISBN:9781450387323
    DOI:10.1145/3477495
    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].

    Sponsors

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 07 July 2022

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. inverse information seeking
    2. market research
    3. virtual agent

    Qualifiers

    • Short-paper

    Conference

    SIGIR '22
    Sponsor:

    Acceptance Rates

    Overall Acceptance Rate 792 of 3,983 submissions, 20%

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)14
    • Downloads (Last 6 weeks)1
    Reflects downloads up to 20 Feb 2025

    Other Metrics

    Citations

    Cited By

    View all
    • (2023)Multimodal Dialogue Understanding via Holistic Modeling and Sequence LabelingNatural Language Processing and Chinese Computing10.1007/978-3-031-44699-3_36(399-411)Online publication date: 12-Oct-2023

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Figures

    Tables

    Media

    Share

    Share

    Share this Publication link

    Share on social media