@inproceedings{cheng-etal-2019-dynamic,
    title = "A Dynamic Speaker Model for Conversational Interactions",
    author = "Cheng, Hao  and
      Fang, Hao  and
      Ostendorf, Mari",
    editor = "Burstein, Jill  and
      Doran, Christy  and
      Solorio, Thamar",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/N19-1284/",
    doi = "10.18653/v1/N19-1284",
    pages = "2772--2785",
    abstract = "Individual differences in speakers are reflected in their language use as well as in their interests and opinions. Characterizing these differences can be useful in human-computer interaction, as well as analysis of human-human conversations. In this work, we introduce a neural model for learning a dynamically updated speaker embedding in a conversational context. Initial model training is unsupervised, using context-sensitive language generation as an objective, with the context being the conversation history. Further fine-tuning can leverage task-dependent supervised training. The learned neural representation of speakers is shown to be useful for content ranking in a socialbot and dialog act prediction in human-human conversations."
}