Using dialogue to learn math in the LeActiveMath project

We describe a tutorial dialogue system under development that assists students in learning how to differentiate equations. The system uses deep natural language understanding and generation to both interpret students ’ utterances and automatically generate a response that is both mathematically correct and adapted pedagogically and linguistically to the local dialogue context. A domain reasoner provides the necessary knowledge about how students should approach math problems as well as their (in)correctness, while a dialogue manager directs pedagogical strategies and keeps track of what needs to be done to keep the dialogue moving along.

LT TTT - A Flexible Tokenisation Tool

We describe LT TTT, a recently developed software system which provides tools to perform text tokenisation and mark-up. The system includes ready-made components to segment text into paragraphs, sentences, words and other kinds of token but, crucially, it also allows users to tailor rule-sets to produce mark-up appropriate for particular applications. We present three case studies of our use of LT TTT: named-entity recognition (MUC-7), citation recognition and mark-up and the preparation of a corpus in the medical domain. We conclude with a discussion of the use of browsers to visualise marked-up text. 1. Introduction The LTG's Text Tokenisation Toolkit (LT TTT, Grover et al., 1999) was developed within an XML processing paradigm whereby tools are combined together in a pipeline allowing each to add, modify or remove some piece of mark-up. The tools are compatible with the LT XML toolset (Thompson et al., 1997) and use the LT XML API to manipulate attribute values and character data ..

Adaptive Tutorial Dialogue Systems Using Deep NLP Techniques

We present tutorial dialogue systems in two different domains that demonstrate the use of dialogue management and deep natural language processing techniques. Generation techniques are used to produce natural sounding feedback adapted to student performance and the dialogue history, and context is used to interpret tentative answers phrased as questions

The Beetle and BeeDiff Tutoring Systems

We describe two tutorial dialogue systems that adapt techniques from task-oriented dialogue systems to tutorial dialogue. Both systems employ the same reusable deep natural language understanding and generation components to interpret students ' written utterances and to automatically generate adaptive tutorial responses, with separate domain reasoners to provide the necessary knowledge about the correctness of student answers and hinting strategies. We focus on integrating the domain-independent language processing components with domain-specific reasoning and tutorial components in order to improve the dialogue interaction, and present a preliminary analysis of BeeDiff's evaluation

Diagnosing natural language answers to support adaptive tutoring

Understanding answers to open-ended explanation questions is important in intelligent tutoring systems. Existing systems use natural language techniques in essay analysis, but revert to scripted interaction with short-answer questions during remediation, making adapting dialogue to individual students difficult. We describe a corpus study that shows that there is a relationship between the types of faulty answers and the remediation strategies that tutors use; that human tutors respond differently to different kinds of correct answers; and that re-stating correct answers is associated with improved learning. We describe a design for a diagnoser based on this study that supports remediation in open-ended questions and provides an analysis of natural language answers that enables adaptive generation of tutorial feedback for both correct and faulty answers