  For conversational AI and virtual assistants to communicate with humans in a
realistic way, they must exhibit human characteristics such as expression of
emotion and personality. Current attempts toward constructing human-like
dialogue agents have presented significant difficulties. We propose Human Level
Attributes (HLAs) based on tropes as the basis of a method for learning
dialogue agents that can imitate the personalities of fictional characters.
Tropes are characteristics of fictional personalities that are observed
recurrently and determined by viewers' impressions. By combining detailed HLA
data with dialogue data for specific characters, we present a dataset,
HLA-Chat, that models character profiles and gives dialogue agents the ability
to learn characters' language styles through their HLAs. We then introduce a
three-component system, ALOHA (which stands for Artificial Learning of Human
Attributes), that combines character space mapping, character community
detection, and language style retrieval to build a character (or personality)
specific language model. Our preliminary experiments demonstrate that two
variations of ALOHA, combined with our proposed dataset, can outperform
baseline models at identifying the correct dialogue responses of chosen target
characters, and are stable regardless of the character's identity, the genre of
the show, and the context of the dialogue.
