Abstract
Query performance predictors estimate a query’s retrieval effectiveness without user feedback. We evaluate the usefulness of pre- and post-retrieval performance predictors for two tasks associated with speech-enabled search: (1) predicting the most effective query transcription from the recognition system’s n-best hypotheses and (2) predicting when to ask the user for a spoken query reformulation. We use machine learning to combine a wide range of query performance predictors as features and evaluate on 5,000 spoken queries collected using a crowdsourced study. Our results suggest that pre- and post-retrieval features are useful for both tasks, and that post-retrieval features are slightly better.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
Our source code and search task descriptions are available at: http://ils.unc.edu/~jarguell/ecir2016/.
- 2.
Participants had to close the pop-up window to continue interacting with the page.
- 3.
References
Aslam, J.A., Pavlu, V.: Query Hardness Estimation Using Jensen-Shannon Divergence Among Multiple Scoring Functions. In: Amati, G., Carpineto, C., Romano, G. (eds.) ECiR 2007. LNCS, vol. 4425, pp. 198–209. Springer, Heidelberg (2007)
Cronen-Townsend, S., Zhou, Y., Croft, W.B.: Predicting query performance. In: SIGIR (2002)
Dang, V., Bendersky, M., Croft, W.B.: Learning to rank query reformulations. In: SIGIR (2010)
Diaz, F.: Performance prediction using spatial autocorrelation. In: SIGIR (2007)
Hauff, C.: Predicting the effectiveness of queries and retrieval systems. dissertation, Univeristy of Twente (2010)
He, B., Ounis, I.: Inferring Query Performance Using Pre-retrieval Predictors. In: Apostolico, A., Melucci, M. (eds.) SPIRE 2004. LNCS, vol. 3246, pp. 43–54. Springer, Heidelberg (2004)
Jiang, J., Jeng, W., He, D.: How do users respond to voice input errors?. SIGIR, Lexical and phonetic query reformulation in voice search. In (2013)
Kumaran, G., Carvalho, V.R.: Reducing long queries using query quality predictors. In: SIGIR (2009)
Li, X., Nguyen, P., Zweig, G., Bohus, D.: Leveraging multiple query logs to improve language models for spoken query recognition. In: ICASSP (2009)
Mamou, J., Sethy, A., Ramabhadran, B., Hoory, R., Vozila, P.: Improved spoken query transcription using co-occurrence information. In: INTERSPEECH (2011)
Peng, F., Roy, S., Shahshahani, B., Beaufays, F.: Search results based n-best hypothesis rescoring with maximum entropy classification. In: IEEE Workshop on Automatic Speech Recognition and Understanding (2013)
Schalkwyk, J., Beeferman, D., Beaufays, F., Byrne, B., Chelba, C., Cohen, M., Kamvar, M., Strope, B.: Your word is my command: Google search by voice: A case study. In: Neustein, A. (ed.) Advances in Speech Recognition. Springer, Heidelberg (2010)
Sheldon, D., Shokouhi, M., Szummer, M., Craswell, N.: Lambdamerge: Merging the results of query reformulations. In: WSDM (2011)
Shtok, A., Kurland, O., Carmel, D., Raiber, F., Markovits, G.: Predicting query performance by query-drift estimation. TOIS, 30(2) (2012)
Smucker, M.D., Allan, J., Carterette, B.: A comparison of statistical significance tests for information retrieval evaluation. In: CIKM (2007)
Xue, X., Huston, S., Croft, W.B.: Improving verbose queries using subset distribution. In: CIKM (2010)
Yom-Tov, E., Fine, S., Carmel, D., Darlow, A.: Learning to estimate query difficulty: Including applications to missing content detection and distributed information retrieval. In: SIGIR (2005)
Zhao, Y., Scholer, F., Tsegay, Y.: Effective Pre-retrieval Query Performance Prediction Using Similarity and Variability Evidence. In: Macdonald, C., Ounis, I., Plachouras, V., Ruthven, I., White, R.W. (eds.) ECIR 2008. LNCS, vol. 4956, pp. 52–64. Springer, Heidelberg (2008)
Zhou, Y., Croft, W.B.: A novel framework to predict query performance. In: CIKM (2006)
Zhou, Y., Croft, W.B.: Query performance prediction in web search environments. In: SIGIR (2007)
Acknowledgments
This work was supported in part by NSF grant IIS-1451668. Any opinions, findings, conclusions, and recommendations expressed in this paper are the authors and do not necessarily reflect those of the sponsors.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Arguello, J., Avula, S., Diaz, F. (2016). Using Query Performance Predictors to Improve Spoken Queries. In: Ferro, N., et al. Advances in Information Retrieval. ECIR 2016. Lecture Notes in Computer Science(), vol 9626. Springer, Cham. https://doi.org/10.1007/978-3-319-30671-1_23
Download citation
DOI: https://doi.org/10.1007/978-3-319-30671-1_23
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-30670-4
Online ISBN: 978-3-319-30671-1
eBook Packages: Computer ScienceComputer Science (R0)