@inproceedings{wang-etal-2022-extracting,
    title = "Extracting and Inferring Personal Attributes from Dialogue",
    author = "Wang, Zhilin  and
      Zhou, Xuhui  and
      Koncel-Kedziorski, Rik  and
      Marin, Alex  and
      Xia, Fei",
    editor = "Liu, Bing  and
      Papangelis, Alexandros  and
      Ultes, Stefan  and
      Rastogi, Abhinav  and
      Chen, Yun-Nung  and
      Spithourakis, Georgios  and
      Nouri, Elnaz  and
      Shi, Weiyan",
    booktitle = "Proceedings of the 4th Workshop on NLP for Conversational AI",
    month = may,
    year = "2022",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2022.nlp4convai-1.6/",
    doi = "10.18653/v1/2022.nlp4convai-1.6",
    pages = "58--69",
    abstract = "Personal attributes represent structured information about a person, such as their hobbies, pets, family, likes and dislikes. We introduce the tasks of extracting and inferring personal attributes from human-human dialogue, and analyze the linguistic demands of these tasks. To meet these challenges, we introduce a simple and extensible model that combines an autoregressive language model utilizing constrained attribute generation with a discriminative reranker. Our model outperforms strong baselines on extracting personal attributes as well as inferring personal attributes that are not contained verbatim in utterances and instead requires commonsense reasoning and lexical inferences, which occur frequently in everyday conversation. Finally, we demonstrate the benefit of incorporating personal attributes in social chit-chat and task-oriented dialogue settings."
}