Learning to Generate Questions with Adaptive Copying Neural Networks
This work addresses the challenge of generating questions from text for applications in natural language processing, representing an incremental improvement over existing methods.
The authors tackled the problem of automatic question generation from sentences and paragraphs by proposing an adaptive copying recurrent neural network model, which outperformed state-of-the-art methods in terms of BLEU and ROUGE scores.
Automatic question generation is an important problem in natural language processing. In this paper we propose a novel adaptive copying recurrent neural network model to tackle the problem of question generation from sentences and paragraphs. The proposed model adds a copying mechanism component onto a bidirectional LSTM architecture to generate more suitable questions adaptively from the input data. Our experimental results show the proposed model can outperform the state-of-the-art question generation methods in terms of BLEU and ROUGE evaluation scores.