Improving Neural Question Generation using Answer Separation

Understand

Neural question generation (NQG) is the task of generating a question from a given passage with deep neural networks.

• Previous NQG models suffer from a problem that a significant proportion of the generated questions include words in the question target, resulting in the generation of unintended questions.
• In this paper, we propose answer-separated seq2seq, which better utilizes the information from both the passage and the target answer.
• By replacing the target answer in the original passage with a special token, our model learns to identify which interrogative word should be used.