Karl Schultz

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Discriminatively trained undirected graphical models have had wide empirical success, and there has been increasing interest in toolkits that ease their application to complex relational data. The power in relational models is in their repeated structure and tied parameters; at issue is how to define these structures in a powerful and flexible way. Rather(More)
In designing and building tutorial dialogue systems it is important not only to understand the tactics employed by human tutors but also to understand how tutors decide when to use various tactics. We argue that these decisions are based not only on student problem-solving steps and the content of student utterances, but also on the meta-communicative(More)
Discriminatively trained undirected graphical models have garnered tremendous interest and empirical success in natural language processing, computer vision, bioinformatics and many other areas [16, 1, 11]. Some of these models use simple structure (e.g. linear-chains, grids, fully-connected affinity graphs), but there has been increasing interest in more(More)
The automatic consolidation of database records from many heterogeneous sources into a single repository requires solving several information integration tasks. Although tasks such as coreference, schema matching, and canonicalization are closely related, they are most commonly studied in isolation. Systems that do tackle multiple integration problems(More)
Scalability and reusability of tutorial dialogue systems is a function of the corresponding characteristics in their component tutoring and dialogue technologies. This paper discusses an architecture for a scalable, reusable, spoken conversational tutor, SCoT. With this design we hope to minimize the efforts needed to reuse the components for implementing a(More)
There has been growing interest in using joint inference across multiple subtasks as a mechanism for avoiding the cascading accumulation of errors in traditional pipelines. Several recent papers demonstrate joint inference between the segmentation of entity mentions and their de-duplication, however, they have various weaknesses: inference information flows(More)
SCoT is a tutorial dialogue system that engages students in natural language discussions through a speech interface. The current instantiation, SCoT-DC, is applied to the domain of shipboard damage control—the task of containing the effects of crises (e.g. fires) that occur aboard Navy vessels. This paper describes a recent evaluation of SCoT-DC and(More)
The ability to lead collaborative discussions and appropriately scaffold learning has been identified as one of the central advantages of human tutorial interaction [6]. In order to reproduce the effectiveness of human tutors, many developers of tutorial dialogue systems have taken the approach of identifying human tutorial tactics and then incorporating(More)