Neural Generation of Diverse Questions using Answer Focus, Contextual and Linguistic Features

TL;DR

This paper introduces a neural question generation model that leverages linguistic features, answer focus, and copying mechanisms to produce diverse, high-quality questions from text, achieving state-of-the-art results.

Contribution

The work presents a novel attentional encoder-decoder model incorporating linguistic features and answer signals for diverse question generation, outperforming previous methods.

Findings

Achieved 19.98 Bleu_4 score on benchmark dataset.

Model generates diverse questions with improved quality.

Human evaluation confirms the effectiveness of added features.

Abstract

Question Generation is the task of automatically creating questions from textual input. In this work we present a new Attentional Encoder--Decoder Recurrent Neural Network model for automatic question generation. Our model incorporates linguistic features and an additional sentence embedding to capture meaning at both sentence and word levels. The linguistic features are designed to capture information related to named entity recognition, word case, and entity coreference resolution. In addition our model uses a copying mechanism and a special answer signal that enables generation of numerous diverse questions on a given sentence. Our model achieves state of the art results of 19.98 Bleu_4 on a benchmark Question Generation dataset, outperforming all previously published results by a significant margin. A human evaluation also shows that these added features improve the quality of the generated questions.