How to Evaluate your Question Answering System Every Day and Still Get Real Work Done

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dc.creatorBreck, Eric
dc.creatorBurger, John D.
dc.creatorFerro, Lisa
dc.creatorHirschman, Lynette
dc.creatorHouse, David
dc.creatorLight, Marc
dc.creatorMani, Inderjeet
dc.date2000-04-17
dc.date.accessioned2026-07-07T03:16:09Z
dc.date.available2026-07-07T03:16:09Z
dc.descriptionIn this paper, we report on Qaviar, an experimental automated evaluation system for question answering applications. The goal of our research was to find an automatically calculated measure that correlates well with human judges' assessment of answer correctness in the context of question answering tasks. Qaviar judges the response by computing recall against the stemmed content words in the human-generated answer key. It counts the answer correct if it exceeds agiven recall threshold. We determined that the answer correctness predicted by Qaviar agreed with the human 93% to 95% of the time. 41 question-answering systems were ranked by both Qaviar and human assessors, and these rankings correlated with a Kendall's Tau measure of 0.920, compared to a correlation of 0.956 between human assessors on the same data.
dc.description6 pages, 3 figures, to appear in Proceedings of the Second International Conference on Language Resources and Evaluation (LREC 2000)
dc.identifierhttps://arxiv.org/abs/cs/0004008
dc.identifierhttp://arxiv.org/abs/cs/0004008
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/30246
dc.subjectComputation and Language
dc.subjectInformation Retrieval
dc.subjectI.2.7; H.3.4
dc.titleHow to Evaluate your Question Answering System Every Day and Still Get Real Work Done
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