FAST - An Automatic Generation System for Grammar Tests

This paper introduces a method for the semi-automatic generation of grammar test items by applying Natural Language Processing (NLP) techniques. Based on manually-designed patterns, sentences gathered from the Web are transformed into tests on grammaticality. The method involves representing test writing knowledge as test patterns, acquiring authentic sentences on the Web, and applying generation strategies to transform sentences into items. At runtime, sentences are converted into two types of TOEFL-style question: multiplechoice and error detection. We also describe a prototype system FAST (Free Assessment of Structural Tests). Evaluation on a set of generated questions indicates that the proposed method performs satisfactory quality. Our methodology provides a promising approach and offers significant potential for computer assisted language learning and assessment.

• Type: Conference
• Year: 2006
• Where: ACL
• Authors: Chia-Yin Chen, Hsien-Chin Liou, Jason S. Chang
• Keywords: ACL 2006, ACL 2007, Authentic Sentences, Grammar Test Items, Test Writing Knowledge