Neural Models for Key Phrase Detection and Question Generation

Abstract

We propose a two-stage neural model to tackle question generation from documents. First, our model estimates the probability that word sequences in a document are ones that a human would pick when selecting candidate answers by training a neural key-phrase extractor on the answers in a question-answering corpus. Predicted key phrases then act as target answers and condition a sequence-to-sequence question-generation model with a copy mechanism. Empirically, our key-phrase extraction model significantly outperforms an entity-tagging baseline and existing rule-based approaches. We further demonstrate that our question generation system formulates fluent, answerable questions from key phrases. This two-stage system could be used to augment or generate reading comprehension datasets, which may be leveraged to improve machine reading systems or in educational settings.

arXiv commentMachine Reading for Question Answering workshop at ACL 2018

Cited by in corpus (4)

• A Unified Query-based Generative Model for Question Generation and Question Answering
• Does Order Matter? An Empirical Study on Generating Multiple Keyphrases as a Sequence
• One Size Does Not Fit All: Generating and Evaluating Variable Number of Keyphrases
• Learning to Ask Questions in Open-domain Conversational Systems with Typed Decoders