  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.
