Paper: Prompt-based Content Scoring for Automated Spoken Language Assessment

ACL ID W13-1721
Title Prompt-based Content Scoring for Automated Spoken Language Assessment
Venue Innovative Use of NLP for Building Educational Applications
Session
Year 2013
Authors

This paper investigates the use of prompt- based content features for the automated as- sessment of spontaneous speech in a spoken language proficiency assessment. The results show that single highest performing prompt- based content feature measures the number of unique lexical types that overlap with the listening materials and are not contained in either the reading materials or a sample re- sponse, with a correlation of r = 0.450 with holistic proficiency scores provided by hu- mans. Furthermore, linear regression scor- ing models that combine the proposed prompt- based content features with additional spoken language proficiency features are shown to achieve competitive performance with scoring models using content features based on pre- scored responses.