Vocabulary-constrained Question Generation with Rare Word Masking and Dual Attention

What is it about?

Question generation is the task of generating questions from a text passage that can be answered using information available in the passage. Known models for question generation are trained to predict words from a large, predefined vocabulary. However, a large vocabulary increases memory usage, training and inference times and a predefined vocabulary may not include context-specific words from the input passage. In this paper, we propose a neural question generation framework that generates semantically accurate and context-specific questions using a small-size vocabulary. We break the question generation task into two subtasks namely, generating the skeletal structure of a question using common words from the vocabulary and pointing to rare words from the input passage to complete the question.

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Photo by Ana Municio on Unsplash

Photo by Ana Municio on Unsplash