Semi-automatic Generation of Multiple-Choice Tests from Mentions of Semantic Relations
Renlong Ai, Sebastian Krause, Walter Cardinal Kasper, Feiyu Xu, Hans Uszkoreit · 2015
We propose a strategy for the semiautomatic generation of learning material for reading-comprehension tests, guided by semantic relations embedded in expository texts.Our approach combines methods from the areas of information extraction and paraphrasing in order to present a language teacher with a set of candidate multiple-choice questions and answers that can be used for verifying a language learners reading capabilities.We implemented a web-based prototype showing the feasibility of our approach and carried out a pilot user evaluation that resulted in encouraging feedback but also pointed out aspects of the strategy and prototype implementation which need improvements.