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.

Rhetorical Structure for Natural Language Generation in Dialogue

Many traditional dialogue systems use simple predicates to send information between a Dialogue Manager and a Natural Language Generation system. We propose a flexible RST-style interface to allow for more complex structures and multimodal output, and we place the first stage of content planning under the control of the dialogue management system with access to asystem-wide information state

Situated Reference in a Hybrid Human-robot Interaction System

We present the situated reference generation module of a hybrid human-robot interaction system that collaborates with a human user in assembling target objects from a wooden toy construction set. The system contains a sub-symbolic goal inference system which is able to detect thegoals and errors of humans by analysing their verbal and non-verbal behaviour. The dialogue manager and reference generation components then use situated references to explain the errors to the human users and provide solution strategies. We describe a user study comparing the results from subjects who heard constant references to those who heard references generated by an adaptive process. There was no difference in the objective results acrossthe two groups, but the subjects in the adaptive condition gave higher subjective ratings to the robot’s abilities as a conversationalpartner. An analysis of the objective and subjective results found that themain predictors of subjective user satisfaction were the user’s performance at the assembly task and the number of times they had to ask for instructions to be repeated

Evaluating description and reference strategies in a cooperative human-robot dialogue system

We present a human-robot dialogue system that enables a robot to work together with a human user to build wooden construction toys. We then describe a study which assessed the responses of naïve users to output that varied along two dimensions: the method of describing an assembly plan (pre-order or post-order), and the method of referring to objects in the world (basic and full). Varying both of these factors produced significant results: subjects using the system that employed a pre-order description strategy asked for instructions to be repeated significantly less often than those who experienced the post-order strategy, while the subjects who heard references generated by the full reference strategy judged the robot’s instructions to be significantly more understandable than did those who heard the output of the basic strategy

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