Natural language understanding and assessment is a subset of natural language processing (NLP). The primary purpose of natural language understanding algorithms is to convert written or spoken human language into representations that can be manipulated by computer programs. Complex learning environments such as intelligent tutoring systems (ITSs) often depend on natural language understanding for fast and accurate interpretation of human language so that the system can respond intelligently in natural language. These ITSs function by interpreting the meaning of student input, assessing the extent to which it manifests learning, and generating suitable feedback to the learner. To operate effectively, systems need to be fast enough to operate in the real time environments of ITSs. Delays in feedback caused by computational processing run the risk of frustrating the user and leading to lower engagement with the system. At the same time, the accuracy of assessing student input is critical because inaccurate feedback can potentially compromise learning and lower the student’s motivation and metacognitive awareness of the learning goals of the system (Millis et al., 2007). As such, student input in ITSs requires an assessment approach that is fast enough to operate in real time but accurate enough to provide appropriate evaluation. One of the ways in which ITSs with natural language understanding verify student input is through matching. In some cases, the match is between the user input and a pre-selected stored answer to a question, solution to a problem, misconception, or other form of benchmark response. In other cases, the system evaluates the degree to which the student input varies from a complex representation or a dynamically computed structure. The computation of matches and similarity metrics are limited by the fidelity and flexibility of the computational linguistics modules. The major challenge with assessing natural language input is that it is relatively unconstrained and rarely follows brittle rules in its computation of spelling, syntax, and semantics (McCarthy et al., 2007). Researchers who have developed tutorial dialogue systems in natural language have explored the accuracy of matching students’ written input to targeted knowledge. Examples of these systems are AutoTutor and Why-Atlas, which tutor students on Newtonian physics (Graesser, Olney, Haynes, & Chipman, 2005; VanLehn , Graesser, et al., 2007), and the iSTART system, which helps students read text at deeper levels (McNamara, Levinstein, & Boonthum, 2004). Systems such as these have typically relied on statistical representations, such as latent semantic analysis (LSA; Landauer, McNamara, Dennis, & Kintsch, 2007) and content word overlap metrics (McNamara, Boonthum, et al., 2007). Indeed, such statistical and word overlap algorithms can boast much success. However, over short dialogue exchanges (such as those in ITSs), the accuracy of interpretation can be seriously compromised without a deeper level of lexico-syntactic textual assessment (McCarthy et al., 2007). Such a lexico-syntactic approach, entailment evaluation, is presented in this chapter. The approach incorporates deeper natural language processing solutions for ITSs with natural language exchanges while remaining sufficiently fast to provide real time assessment of user input.
Extensible Chatbot Architecture Using Metamodels of Natural Language Understanding