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.
bibtex
Copied!
@inproceedings{arguello:ecir2017,
year = {2017},
title = {Using Query Performance Predictors to Reduce Spoken Queries},
publisher = {Springer International Publishing},
pages = {27--39},
isbn = {978-3-319-56608-5},
editor = {Jose, Joemon M and Hauff, Claudia and Alt{\i}ngovde, Ismail Sengor and Song, Dawei and Albakour, Dyaa and Watt, Stuart and Tait, John},
booktitle = {Advances in Information Retrieval},
author = {Arguello, Jaime and Avula, Sandeep and Diaz, Fernando},
address = {Cham}
}