research-article

Discovering customer intent in real-time for streamlining service desk conversations

Authors:

Tanveer Faruquie,

L. Venkata Subramaniam,

Ganesh RamakrishnanAuthors Info & Claims

CIKM '11: Proceedings of the 20th ACM international conference on Information and knowledge management

Pages 1383 - 1388

https://doi.org/10.1145/2063576.2063776

Published: 24 October 2011 Publication History

Abstract

Businesses require the contact center agents to meet pre-specified customer satisfaction levels while keeping the cost of operations low or meeting sales targets, objectives that end up being complementary and difficult to achieve in real-time. In this paper, we describe a speech enabled real-time conversation management system that tracks customer-agent conversations to detect user intent (e.g. gathering information, likely to buy, etc.) that can help agents to then decide the best sequence of actions for that call. We present an entropy based decision support system that parses a text stream generated in real-time during a audio conversation and identifies the first instance at which the intent becomes distinct enough for the agent to then take subsequent actions. We provide evaluation results displaying the efficiency and effectiveness of our system.

References

[1]

S. Agarwal, S. Godbole, D. Punjani, S. Roy. 2007. How Much Noise Is Too Much: A Study in Automatic Text Classification. Proceedings of IEEE International Conference on Data Mining, Omaha, Nebraska, 3--12.

Digital Library

[2]

C. Aggarwal, J. Han, J. Wang, P. S. Yu. 2004. On Demand classification of data streams. Proceedings of the International Conferece on Knowledge Discovery and Data Mining, Seattle, USA, 503--508.

Digital Library

[3]

L. Cohen, G. Avrahami, M. Last, A. Kandel, O. Kipersztok. 2005. Incremental classification of nonstationary data streams. ECML/PKDD-2005 International Workshop on Knowledge Discovery from Data Streams, Portugal.

[4]

P. Domingos, G. Hulten. 2000. Mining High-Speed Data Streams. Proceedings of International Conference on Knowledge Discovery and Data Mining, 71--80.

Digital Library

[5]

Y. Freund, R. E. Schapire. 1996. Experiments in new boosting algorithm. Prodeedings of the Thirteenth International Conference on Machine Learning, 148--156.

[6]

J. R. Glass, T. J. Hazen, I. L. Hetherington. 1999. Real-time telephone-based speech recognition in the Jupiter domain. Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, Phoenix, Arizona, 61--64.

Digital Library

[7]

M. A. Hearst. 1994. Multi-paragraph segmentation of expository text. Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, Phoenix, Arizona, 61--64.

Digital Library

[8]

G. Hulten, L. Spencer, P. Domingos. 2001. Mining Time-Changing Data Streams. Proceedings of the Annual Meeting of the Association for Computer Linguistics (ACL), 9--16.

Digital Library

[9]

I. Katakis, G. Tsoumakas, I. Vlahavas. 2006. Dynamic feature space and incremental feature selection for the classification of textual data streams. ECML/PKDD-2006 International Workshop on Knowledge Discovery from Data Streams, Berlin, Germany, 107--116.

[10]

R. Klinkenberg, T. Joachims. 2000. Detecting concept drift with support vector machines. Proceedings of International Conference on Machine Learning, 487--494.

Digital Library

[11]

K. Kummamuru, Deepak P., S. Roy, L. V. Subramaniam. 2008. Unsupervised Segmentation of Conversational Transcripts. Proceedings of SIAM International Conference on Data Mining, 834--845.

[12]

H.-K. J. Kuo, C.-H. Lee. 2003. Discriminative training of natural language call routers. IEEE Trans. on Speech and Audio Processing, 11(1), 24--35.

[13]

N. Landwehr, M. Hall, F. Eibe. 2003. Logistic Model Trees. Proceedings of European Conference on Machine Learning, Cavtat-Dubrovnik, Croatia, 241--252.

Digital Library

[14]

Y. Park. 2007. Automatic call section segmentation for contact-center calls. Proceedings of International Conference on Knowledge Discovery and Data Mining, Lisbon, Portugal, 117--126.

Digital Library

[15]

H. Takeuchi, L. V. Subramaniam, T. Nasukawa, S. Roy. 2007. Automatic Identification of Important Segments and Expressions for Mining of Business-Oriented Conversations at Contact Centers. Proceedings of Empirical Methods on Natural Language Processing, Prague, Czech Republic.

[16]

M. Tang, B. Pellom, K. Hacioglu. 2003. Calltype classification and unsupervised training for the call center domain. Proceedings of the Automatic Speech Recog. and Understanding Workshop, St. Thomas, U S Virgin Islands, 204--208.

[17]

M. Padmanabhan, G. Saon, J. Huang, B. Kingsbury, and L. Mangu. Automatic Speech Recognition Performance on a Voicemail Transcription Task. IEEE Trans. on Speech and Audio Processing, 10(7):433--442, 2002.

[18]

H. Chevalier, C. Ingold, C. Kunz, C. Moore, C. Roven, J. Yamron,B. Baker, P. Bamberg, S. Bridle, T. Bruce, and A. Weader. Large-vocabulary speech recognition in specialized domains. ICASSP-95, May 9--12, 1995, Detroit, MI, USA.

[19]

G. Widmer, M. Kubat. 1996. Learning in the presence of concept drift and hidden contexts. Machine Learning, 23(1), 69--101.

[20]

V. N. Vapnik. 1995. The nature of statistical learning theory. Springer-Verlag, New York.

Digital Library

[21]

U. Nambiar, H. Gupta, R. Balakrishnan and M. Mohania. 2008. Helping Satisfy Multiple Objectives during a Service Desk Conversation. SIGMOD, June 9--12, 2008, Vancouver, BC, Canada.

Digital Library

[22]

T. Mitchell. 1997 Machine Learning. McGraw Hill.

Digital Library

Cited By

Gracia CAnguera XLuque JArtzi I(2014)Phoneme-Lattice to Phoneme-Sequence Matching Algorithm Based on Dynamic ProgrammingProceedings of the Second International Conference on Advances in Speech and Language Technologies for Iberian Languages - Volume 885410.1007/978-3-319-13623-3_11(99-108)Online publication date: 19-Nov-2014
https://dl.acm.org/doi/10.1007/978-3-319-13623-3_11
Ikbal SVerma AGhosh PChurch KMarcus J(2013)Intent focused summarization of caller-agent conversations2013 IEEE International Conference on Acoustics, Speech and Signal Processing10.1109/ICASSP.2013.6639294(8352-8356)Online publication date: May-2013
https://doi.org/10.1109/ICASSP.2013.6639294

Index Terms

Discovering customer intent in real-time for streamlining service desk conversations
1. Information systems
  1. Information retrieval

Recommendations

Artificial conversations for customer service chatter bots

We describe limitations of the current generation of computer-based chatter-bots.We demonstrate structures and algorithms producing computer-based conversations.Our conversations combine semantic and pragmatic knowledge for customer service.Our computer-...
MCIC: Multimodal Conversational Intent Classification for E-commerce Customer Service
Natural Language Processing and Chinese Computing
Abstract
Conversational intent classification (CIC) plays a significant role in dialogue understanding, and most previous works only focus on the text modality. Nevertheless, in real conversations of E-commerce customer service, users often send images (...
Analyzing and Characterizing User Intent in Information-seeking Conversations
SIGIR '18: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval

Understanding and characterizing how people interact in information-seeking conversations is crucial in developing conversational search systems. In this paper, we introduce a new dataset designed for this purpose and use it to analyze information-...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

CIKM '11: Proceedings of the 20th ACM international conference on Information and knowledge management

October 2011

2712 pages

ISBN:9781450307178

DOI:10.1145/2063576

Editors:
Bettina Berendt,
Arjen de Vries,
Wenfei Fan,
Craig Macdonald
University of Glasgow, UK
,
Iadh Ounis
University of Glasgow, UK
,
Ian Ruthven
University of Strathclyde, UK

Copyright © 2011 ACM.

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]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 October 2011

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

CIKM '11

Sponsor:

CIKM '11: International Conference on Information and Knowledge Management

October 24 - 28, 2011

Glasgow, Scotland, UK

Acceptance Rates

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

2
Total Citations
View Citations
267
Total Downloads

Downloads (Last 12 months)7
Downloads (Last 6 weeks)0

Reflects downloads up to 02 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Gracia CAnguera XLuque JArtzi I(2014)Phoneme-Lattice to Phoneme-Sequence Matching Algorithm Based on Dynamic ProgrammingProceedings of the Second International Conference on Advances in Speech and Language Technologies for Iberian Languages - Volume 885410.1007/978-3-319-13623-3_11(99-108)Online publication date: 19-Nov-2014
https://dl.acm.org/doi/10.1007/978-3-319-13623-3_11
Ikbal SVerma AGhosh PChurch KMarcus J(2013)Intent focused summarization of caller-agent conversations2013 IEEE International Conference on Acoustics, Speech and Signal Processing10.1109/ICASSP.2013.6639294(8352-8356)Online publication date: May-2013
https://doi.org/10.1109/ICASSP.2013.6639294

View Options

Login options

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

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten