{
  "id": "1705.02012",
  "version": "v1",
  "published": "2017-05-04T20:58:06.000Z",
  "updated": "2017-05-04T20:58:06.000Z",
  "title": "Machine Comprehension by Text-to-Text Neural Question Generation",
  "authors": [
    "Xingdi Yuan",
    "Tong Wang",
    "Caglar Gulcehre",
    "Alessandro Sordoni",
    "Philip Bachman",
    "Sandeep Subramanian",
    "Saizheng Zhang",
    "Adam Trischler"
  ],
  "categories": [
    "cs.CL"
  ],
  "abstract": "We propose a recurrent neural model that generates natural-language questions from documents, conditioned on answers. We show how to train the model using a combination of supervised and reinforcement learning. After teacher forcing for standard maximum likelihood training, we fine-tune the model using policy gradient techniques to maximize several rewards that measure question quality. Most notably, one of these rewards is the performance of a question-answering system. We motivate question generation as a means to improve the performance of question answering systems. Our model is trained and evaluated on the recent question-answering dataset SQuAD.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2017-05-04T20:58:06.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "text-to-text neural question generation",
      "machine comprehension",
      "measure question quality",
      "recurrent neural model",
      "policy gradient techniques"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}