Dr. Gregory Sanders
Multimodal Information Group (894.01), of the
Information Access Division, (894), of the
Information Technology Laboratory, of the
National Institute of Standards and Technology (NIST), an agency of the
U.S. Department of Commerce
Computer Scientist, Multimodal Information Group
Most of my current work focuses on designing metrics and evaluations for speech-to-speech machine translation, in the DARPA TRANSTAC program.
B.A. Millikin University (Music)
M.S. and Ph.D. Illinois Institute of Technology (Computer Science)
Publications as first author
Sanders, G., Bronsart, S., and Schlenoff, C., 2008. Odds of Successful Transfer of low-level concepts: A key metric for bidirectional speech-to-speech machine translation in DARPA's TRANSTAC program. Proceedings of LREC 2008. The paper is available here as a .pdf document.
Sanders, G. A., and Le, A. N., 2004. Effects of Speech Recognition Accuracy on the Performance of DARPA Communicator Spoken Dialogue Systems. International Journal of Speech Technology, 7:293-309. The submitted manuscript is available here as a PostScript or .pdf document (note, manuscript begins with a blank page).
Sanders, G. A., Le, A. N., and Garofolo, J. S., 2002. Effects of Word Error Rate in the DARPA Communicator Data During 2000 and 2001. ICSLP-2002, 7th International Conference on Spoken Language Processing, Denver, Colorado: International Speech Communication Association, pp. 277-280. The paper is available here as a .pdf document.
Sanders, G. A., and Scholtz, J., 2000. Measurement and Evaluation of Embodied Conversational Agents. [Chapter 12 of] Cassell, J., Sullivan, J., Prevost, S., and Churchill, E., eds., 2000. Embodied Conversational Agents. MIT Press, 2000, ISBN 0-262-03278-3.
Sanders, G. A., and Scholtz, J., 1998. Measurement and Evaluation in Embodied Conversational Characters. Proceedings of the Workshop on Embodied Conversational Characters (WECC 98), Tahoe City, California.
Publications as co-author
Condon, S., Phillips, J., Doran, C., Aberdeen, J., Parvaz, D., Oshika, B., Sanders, G., and Schlenoff, C., 2008. Applying automated metrics to speech translation dialogs. Proceedings of LREC 2008.
Weiss, B., Schlenoff, C., Sanders, G. Steves, M. P., Condon, S., Phillips, J., and Parvaz, D., 2008. Performance evaluation of speech translation systems. Proceedings of LREC 2008.
Sanders, G. A., 1995. Generation of Explanations and Multi-Turn Discourse Structures in Tutorial Dialogue, based on Transcript Analysis. Unpublished doctoral dissertation, Illinois Institute of Technology, Chicago, Illinois.
My Ph.D. work at IIT focused on generating the discourse and dialogue structure of tutorial dialogues in an intelligent tutoring system.
Perhaps the main effect of my dissertation was its introduction of the term "Directed Line of Reasoning" (or DLR), which has since been adopted by other researchers. I was certainly not the first to describe the phenomenon, but I think I was the first to explain (in detail) how an intelligent tutoring system could produce them, and do so covering the appropriate material, at the appropriate time, in the appropriate form, for the appropriate purposes.
In a tutoring dialogue, a "Directed Line of Reasoning" is a series of bite-sized leading questions from the tutor and answers from the student, through which the tutor intends to evoke correct cause-and-effect reasoning from the student, based on material the student plausibly already knows. DLRs thus appear when the student plausibly already knows all the steps. DLRs play various roles in tutoring sessions. They may serve as hints, where the tutor leads the student toward (or even to) something the student did not manage to produce (put together) without help. They may serve as a summary, allowing the tutor to verify that the student really knows all the steps, and reviewing the sequence of steps for the student (a useful tactic after a complicated stretch of tutoring the individual steps). When a student already "almost" knows the content, they often serve as an explanation, or even as a method of exposition. The key merit of a DLR as a tutoring tactic is that it requires the student to play a maximally active role, and, when done well, a DLR requires the student to produce essentially the entire cause-and-effect explanation independently. Gregory Hume has analyzed the uses of this tactic in great depth.
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