Meta Sequence Learning for Generating Adequate Question-Answer Pairs
This addresses the need for automated question generation in educational assessments, but it is incremental as it builds on existing NLP techniques.
The paper tackled the problem of generating multiple-choice questions for reading comprehension by introducing MetaQA, a method that uses meta-sequence representations to create question-answer pairs from declarative sentences, achieving over 97% accuracy on SAT practice tests.
Creating multiple-choice questions to assess reading comprehension of a given article involves generating question-answer pairs (QAPs) on the main points of the document. We present a learning scheme to generate adequate QAPs via meta-sequence representations of sentences. A meta sequence is a sequence of vectors comprising semantic and syntactic tags. In particular, we devise a scheme called MetaQA to learn meta sequences from training data to form pairs of a meta sequence for a declarative sentence (MD) and a corresponding interrogative sentence (MIs). On a given declarative sentence, a trained MetaQA model converts it to a meta sequence, finds a matched MD, and uses the corresponding MIs and the input sentence to generate QAPs. We implement MetaQA for the English language using semantic-role labeling, part-of-speech tagging, and named-entity recognition, and show that trained on a small dataset, MetaQA generates efficiently over the official SAT practice reading tests a large number of syntactically and semantically correct QAPs with over 97\% accuracy.