ALOHA: Artificial Learning of Human Attributes for Dialogue Agents
This work addresses the challenge of making conversational AI more human-like for applications in virtual assistants and entertainment, though it is incremental as it builds on existing methods for personality modeling.
The paper tackles the problem of creating dialogue agents that imitate fictional characters' personalities by introducing Human Level Attributes (HLAs) based on tropes and a dataset called HLA-Chat, resulting in ALOHA, a system that outperforms baseline models in identifying correct dialogue responses for target characters.
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.