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 than using a declarative language, such as SQL or first-order logic, we advocate using an imperative language to express various aspects of model structure, inference, and learning. By combining the traditional, declarative, statistical semantics of factor graphs with imperative definitions of their construction and operation, we allow the user to mix declarative and procedural domain knowledge, and also gain significant efficiencies. We have implemented such imperatively defined factor graphs in a system we call Factorie, a software library for an object-oriented, strongly-typed, functional language. In experimental comparisons to Markov Logic Networks on joint segmentation and coreference, we find our approach to be 3-15 times faster while reducing error by 20-25%-achieving a new state of the art.

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 traditionally solve each independently, allowing errors to propagate from one task to another. In this paper, we describe a discriminatively-trained model that reasons about schema matching, coreference, and canonicalization jointly. We evaluate our model on a real-world data set of people and demonstrate that simultaneously solving these tasks reduces errors over a cascaded or isolated approach. Our experiments show that a joint model is able to improve substantially over systems that either solve each task in isolation or with the conventional cascade. We demonstrate nearly a 50% error reduction for coreference and a 40% error reduction for schema matching.

Contextualizing learning in an intelligent tutoring system is difficult for many reasons. Goals such as presenting material in an understandable manner, minimizing confusion and frustration, and helping the student reason about their actions all need to be balanced. Previous research has shown reflective discussions (with human tutors) occurring after problem-solving to be effective in helping students reason about their own actions (S. Katz et. al, 2003). However, leading a reflective discussion makes it difficult to present information in an understandable manner, and without contextualization, it is easy to create student confusion and frustration. This raises the question: how can intelligent tutoring systems effectively contextualize learning in a reflective discussion? In this paper, we describe the tutorial architecture of SCoT, a spoken conversational tutor that uses flexible, adaptive planning and multi-modal task modeling to support the contextualization of learning in reflective dialogues.