Swedish Question Generation for Assessing Reading Comprehension
The objective of the Swedish Question Generation for Assessing Reading Comprehension (SWE-QUEST) project is to develop a demo system that, given a text, automatically generates multiple-choice reading comprehension questions on the text, as shown in the picture below.
The demo system will generate the whole multiple-choice question: both the question itself, and the answer alternatives, using a neural-network-based generative model. Although the example above was given in English, our demo system will work for Swedish text. The project will push the state-of-the-art in natural language generation. The system is intended to be used by teachers of SFI (Svenska för invandrare), in order to facilitate test construction and the development of teaching materials, but can also be used for self-studies of Swedish.
The SFI students form a heterogeneous group and the SFI classes are often too big to allow the teacher time to adapt the level of teaching to cater to each student individually, therefore developing such a tool could be highly useful. It would rapidly and easily generate a number of suggested multiple-choice questions (MCQ) on a text material, to be used in teaching and assessment.
The task of automatically generating reading comprehension questions (without distractors) using neural methods has been studied before, primarily for English. There has also been some attempts at generating distractors, given the question and the correct answer, using neural methods. However, no attempt has so far been made to construct a trainable model that generates the whole MCQ in one go.
The researchers in the team represent the KTH Schools of Electrical Engineering and Computer Science, the division of Speech, Music and Hearing and the Department of Swedish Language and Multilingualism at Stockholm University.
Watch the recorded presentation at Digitalize in Stockholm 2022 event:
Associate professor at KTH EECS School, PII of project SWE-QUEST: Swedish Question Generation for Assessing Reading Comprehension, Digital Futures Faculty+46 8 790 75 63