Extended Answer and Uncertainty Aware Neural Question Generation

TL;DR

This paper introduces an advanced neural question generation model that improves answer representation and balances copying and generating words, leading to better performance on the SQuAD dataset.

Contribution

It proposes the Extended Answer-aware Network with Word-based Coverage Mechanism and Uncertainty-aware Beam Search for improved question generation.

Findings

Significant performance improvement on SQuAD dataset

Effective handling of answer representation and repetition issues

Balanced copying and generation in question formation

Abstract

In this paper, we study automatic question generation, the task of creating questions from corresponding text passages where some certain spans of the text can serve as the answers. We propose an Extended Answer-aware Network (EAN) which is trained with Word-based Coverage Mechanism (WCM) and decodes with Uncertainty-aware Beam Search (UBS). The EAN represents the target answer by its surrounding sentence with an encoder, and incorporates the information of the extended answer into paragraph representation with gated paragraph-to-answer attention to tackle the problem of the inadequate representation of the target answer. To reduce undesirable repetition, the WCM penalizes repeatedly attending to the same words at different time-steps in the training stage. The UBS aims to seek a better balance between the model confidence in copying words from an input text paragraph and the confidence in generating words from a vocabulary. We conduct experiments on the SQuAD dataset, and the results show our approach achieves significant performance improvement.