Teaching Machines to Ask Questions

By Kaichun Yao, Libo Zhang, Tiejian Luo, Lili Tao, Yanjun Wu. 2018

48 citations recorded by the source corpus.

We propose a novel neural network model that aims to generate diverse and human-like natural language questions. Our model not only directly captures the variability in possible questions by using a latent variable, but also generates certain types of questions by introducing an additional observed variable. We deploy our model in the generative adversarial network (GAN) framework and modify the discriminator which not only allows evaluating the question authenticity, but predicts the question type. Our model is trained and evaluated on a question-answering dataset SQuAD, and the experimental results shown the proposed model is able to generate diverse and readable questions with the specific attribute.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Question Answering & Reading Comprehension

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Cordelia Schmid
2. Ivan Laptev
3. Josef Sivic
4. Andrew Zisserman
5. Antoine Miech
6. Bernt Schiele
7. Luc Van Gool
8. Mohit Bansal

In Computer Vision and Pattern Recognition

1. Cordelia Schmid
2. Luc Van Gool
3. Andrew Zisserman
4. Ivan Laptev
5. Andrea Vedaldi
6. Jitendra Malik
7. Yi Yang
8. Trevor Darrell

In Question Answering & Reading Comprehension

1. Ivan Laptev
2. Josef Sivic
3. Antoine Miech
4. Antoine Yang
5. Cordelia Schmid

Citations in the corpus

• Just Ask: Learning to Answer Questions from Millions of Narrated Videos (2020, positive): “Moreover, rule-based generation requires the manual creation of rules by experts which is expensive, and has also been recently outperformed by neural question generation [20, 89, 99] as used in our approach.”
• Putting the Horse before the Cart: A Generator-Evaluator Framework for Question Generation from Text (2018, background): “(Yao et al., 2018) proposed generative adversarial network (GAN) framework with modified discriminator to predict question type.”
• Learning to Generate Questions by LearningWhat not to Generate (2019, methodology): “the question. •SeqCopyNet [43] proposed a method to improve the copying mechanism in Seq2Seq models by copying not only a single word but a sequence of words from the input sentence. •seq2seq+z+c+GAN [37] proposed a model employed in GAN framework using the latent variable to capture the diversity a”
• Modeling question asking using neural program generation (2019, background): “Recently, neural network approaches have also been proposed, including retrieval methods which select the best question from past experience [12] and encoder-decoder frameworks which map visual or linguistic inputs to questions [18, 12, 23, 21].”