Joshua B. Gordon

Also published as: Joshua Gordon


2011

pdf bib
Embedded Wizardry
Rebecca J. Passonneau | Susan L. Epstein | Tiziana Ligorio | Joshua Gordon
Proceedings of the SIGDIAL 2011 Conference

pdf bib
Learning to Balance Grounding Rationales for Dialogue Systems
Joshua Gordon | Rebecca J. Passonneau | Susan L. Epstein
Proceedings of the SIGDIAL 2011 Conference

2010

pdf bib
Learning about Voice Search for Spoken Dialogue Systems
Rebecca Passonneau | Susan L. Epstein | Tiziana Ligorio | Joshua B. Gordon | Pravin Bhutada
Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics

pdf bib
An Evaluation Framework for Natural Language Understanding in Spoken Dialogue Systems
Joshua B. Gordon | Rebecca J. Passonneau
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

We present an evaluation framework to enable developers of information seeking, transaction based spoken dialogue systems to compare the robustness of natural language understanding (NLU) approaches across varying levels of word error rate and contrasting domains. We develop statistical and semantic parsing based approaches to dialogue act identification and concept retrieval. Voice search is used in each approach to ultimately query the database. Included in the framework is a method for developers to bootstrap a representative pseudo-corpus, which is used to estimate NLU performance in a new domain. We illustrate the relative merits of these NLU techniques by contrasting our statistical NLU approach with a semantic parsing method over two contrasting applications, our CheckItOut library system and the deployed Let’s Go Public! system, across four levels of word error rate. We find that with respect to both dialogue act identification and concept retrieval, our statistical NLU approach is more likely to robustly accommodate the freer form, less constrained utterances of CheckItOut at higher word error rates than is possible with semantic parsing.