Neural Generation of Diverse Questions using Answer Focus, Contextual and Linguistic Features
This work addresses the need for automated question generation in educational or QA systems, representing a strong incremental improvement over existing methods.
The authors tackled the problem of automatically generating diverse questions from text by introducing an attentional encoder-decoder RNN model that incorporates linguistic features and an answer signal, achieving a state-of-the-art BLEU-4 score of 19.98 on a benchmark dataset.
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.