@inproceedings{cho-etal-2019-towards,
    title = "Towards Coherent and Cohesive Long-form Text Generation",
    author = "Cho, Woon Sang  and
      Zhang, Pengchuan  and
      Zhang, Yizhe  and
      Li, Xiujun  and
      Galley, Michel  and
      Brockett, Chris  and
      Wang, Mengdi  and
      Gao, Jianfeng",
    editor = "Bamman, David  and
      Chaturvedi, Snigdha  and
      Clark, Elizabeth  and
      Fiterau, Madalina  and
      Iyyer, Mohit",
    booktitle = "Proceedings of the First Workshop on Narrative Understanding",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W19-2401",
    doi = "10.18653/v1/W19-2401",
    pages = "1--11",
    abstract = "Generating coherent and cohesive long-form texts is a challenging task. Previous works relied on large amounts of human-generated texts to train neural language models. However, few attempted to explicitly improve neural language models from the perspectives of coherence and cohesion. In this work, we propose a new neural language model that is equipped with two neural discriminators which provide feedback signals at the levels of sentence (cohesion) and paragraph (coherence). Our model is trained using a simple yet efficient variant of policy gradient, called {`}negative-critical sequence training{'}, which is proposed to eliminate the need of training a separate critic for estimating {`}baseline{'}. Results demonstrate the effectiveness of our approach, showing improvements over the strong baseline {--} recurrent attention-based bidirectional MLE-trained neural language model.",
}