@inproceedings{gu-etal-2021-chaincqg,
    title = "{C}hain{CQG}: Flow-Aware Conversational Question Generation",
    author = "Gu, Jing  and
      Mirshekari, Mostafa  and
      Yu, Zhou  and
      Sisto, Aaron",
    editor = "Merlo, Paola  and
      Tiedemann, Jorg  and
      Tsarfaty, Reut",
    booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume",
    month = apr,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Author-page-Marten-During-lu/2021.eacl-main.177/",
    doi = "10.18653/v1/2021.eacl-main.177",
    pages = "2061--2070",
    abstract = "Conversational systems enable numerous valuable applications, and question-answering is an important component underlying many of these. However, conversational question-answering remains challenging due to the lack of realistic, domain-specific training data. Inspired by this bottleneck, we focus on conversational question generation as a means to generate synthetic conversations for training and evaluation purposes. We present a number of novel strategies to improve conversational flow and accommodate varying question types and overall fluidity. Specifically, we design ChainCQG as a two-stage architecture that learns question-answer representations across multiple dialogue turns using a flow propagation training strategy. ChainCQG significantly outperforms both answer-aware and answer-unaware SOTA baselines (e.g., up to 48{\%} BLEU-1 improvement). Additionally, our model is able to generate different types of questions, with improved fluidity and coreference alignment."
}