Paper: Applying Unsupervised Learning To Support Vector Space Model Based Speaking Assessment

ACL ID W13-1707
Title Applying Unsupervised Learning To Support Vector Space Model Based Speaking Assessment
Venue Innovative Use of NLP for Building Educational Applications
Session
Year 2013
Authors

Vector Space Models (VSM) have been widely used in the language assessment field to provide measurements of students? vocab- ulary choices and content relevancy. How- ever, training reference vectors (RV) in a VSM requires a time-consuming and costly human scoring process. To address this limitation, we applied unsupervised learning methods to re- duce or even eliminate the human scoring step required for training RVs. Our experiments conducted on data from a non-native English speaking test suggest that the unsupervised topic clustering is better at selecting responses to train RVs than random selection. In addi- tion, we conducted an experiment to totally eliminate the need of human scoring. Instead of using human rated scores to train RVs, we used used the machine-predicted scores from ...