book.bib

@article{zichaowang,
  title = {QG-Net: A Data-Driven Question Generation Model for Educational Content},
  author = {Zichao Wang},
  year = {2018},
  url = {http://www.princeton.edu/~shitingl/papers/18l@s-qgen.pdf},
}

@article{anonymous,
  title = {Topic-based Question Generation},
  author = {Anonymous authors},
  year = {2018},
  url = {https://openreview.net/pdf?id=rk3pnae0b},
}

@article{vishwajeet,
  title = {A Framework for Automatic Question Generation from Text using Deep Reinforcement Learning},
  author = {Vishwajeet Kumar},
  year = {2018},
  url = {https://arxiv.org/pdf/1808.04961.pdf},
}

@article{yalong,
  title = {Deep Attention Neural Tensor Network for Visual Question Answering},
  author = {Yalong Bai},
  year = {2018},
  url = {http://openaccess.thecvf.com/content_ECCV_2018/papers/Yalong_Bai_Deep_Attention_Neural_ECCV_2018_paper.pdf},
}

@article{yangshi,
  title = {Question Type Guided Attention in Visual Question Answering},
  author = {Yang Shi},
  year = {2018},
  url = {https://arxiv.org/pdf/1804.02088.pdf},
}

@article{zhaozhou,
  title = {Multi-Turn Video Question Answering via Multi-Stream Hierarchical Attention Context Network},
  author = {Zhou Zhao},
  year = {2018},
  url = {https://www.ijcai.org/proceedings/2018/0513.pdf},
}

@article{makarand,
  title = {MovieQA: Understanding Stories in Movies through Question-Answering},
  author = {Makarand Tapaswi},
  year = {2016},
  url = {https://arxiv.org/pdf/1512.02902.pdf},
}

@article{xiaohan,
  title = {Dual Ask-Answer Network for Machine Reading Comprehension},
  author = {Han Xiao},
  year = {2018},
  url = {https://arxiv.org/pdf/1809.01997.pdf},
}

@article{duxinya,
  title = {Harvesting Paragraph-Level Question-Answer Pairs from Wikipedia},
  author = {Xinya Du},
  year = {2018},
  url = {https://arxiv.org/pdf/1805.05942.pdf},
}

@article{liyikang,
  title = {Visual Question Generation as Dual Task of Visual Question Answering},
  author = {Yikang Li},
  year = {2018},
  url = {http://cvboy.com/pdf/publications/cvpr2018_iqan.pdf},
}

@article{songlinfeng,
  title = {A Unified Query-based Generative Model for Question Generation and Question Answering},
  author = {Linfeng Song},
  year = {2018},
  url = {https://arxiv.org/pdf/1709.01058.pdf},
}

@article{youmna,
  title = {Neural Automated Essay Scoring and Coherence Modeling for Adversarially Crafted Input},
  author = {Youmna Farag},
  year = {2018},
  url = {http://aclweb.org/anthology/N18-1024},
}


@article{zhaozhou,
  title = {Open-Ended Long-form Video Question Answering via Adaptive Hierarchical Reinforced Networks},
  author = {Zhou Zhao},
  year = {2018},
  url = {https://www.ijcai.org/proceedings/2018/0512.pdf},
}


@article{oluwatobi,
  title = {Multi-turn Dialogue Response Generation in an Adversarial Learning Framework},
  author = {Oluwatobi O. Olabiyi},
  year = {2018},
  url = {https://arxiv.org/pdf/1805.11752.pdf},
}


@article{youtube8m,
  title = {YouTube-8M Dataset},
  author = {Sami Abu-El-Haija},
  year = {2017},
  url = {https://research.google.com/youtube8m/},
}


@article{becky,
  title = {Video Captions for Online Courses: Do YouTube’s Auto-generated Captions Meet Deaf Students’ Needs?},
  author = {Becky Sue Parton},
  year = {2016},
  url = {http://jofdl.nz/index.php/JOFDL/article/download/255/198},
}

@article{videomcc,
  author    = {Du Tran and 
               Maksim Bolonkin and
               Manohar Paluri and
               Lorenzo Torresani},
  title     = {VideoMCC: a New Benchmark for Video Comprehension},
  journal   = {CoRR},
  volume    = {abs/1606.07373},
  year      = {2016},
  url       = {http://arxiv.org/abs/1606.07373}
}

@article{nayyer,
  title = {Video Description: A Survey of Methods, Datasets and Evaluation Metrics},
  author = {Nayyer Aafaq},
  year = {2018},
  url = {https://arxiv.org/pdf/1806.00186.pdf},
}


@article{eunsol,
  title = {QuAC : Question Answering in Context},
  author = {Eunsol Choi},
  year = {2018},
  url = {https://arxiv.org/pdf/1808.07036.pdf},
}

@article{googlekg,
  title = {Google Knowledge Graph},
  author = {Google},
  year = {2015},
  url = {https://developers.google.com/knowledge-graph/},
}


@article{knowedu,
  title = {KnowEdu: A System to Construct Knowledge Graph for Education},
  author = {Penghe Chen},
  year = {2018},
  url = {https://ieeexplore.ieee.org/document/8362657},
}


@article{liuqiao,
  title = {Generalized Embedding Model for Knowledge Graph Mining},
  author = {Qiao Liu},
  year = {2018},
  url = {http://www.mlgworkshop.org/2018/papers/MLG2018_paper_5.pdf},
}

@article{anony,
  title = {Probabilistic Knowledge Graph Embeddings},
  author = {Anonymous authors},
  year = {2019},
  url = {https://openreview.net/pdf?id=rJ4qXnCqFX},
}

@article{dongxinluna,
  title = {Knowledge Vault: A Web-Scale Approach to Probabilistic Knowledge Fusion},
  author = {Xin Luna Dong},
  year = {2014},
  url = {https://dejanseo.com.au/wp-content/uploads/2014/08/Knowledge-Vault-A-Web-Scale-Approach-to-Probabilistic-Knowledge-Fusion.pdf},
}


@article{structureddata,
  title = {Understand how structured data works},
  author = {Google},
  year = {2018},
  url = {https://developers.google.com/search/docs/guides/intro-structured-data},
}


@article{qa_imp,
  title = {How does the [current] best question answering model work?},
  author = {Simeon Kostadinov},
  year = {2017},
  url = {https://towardsdatascience.com/how-the-current-best-question-answering-model-works-8bbacf375e2a},
}