1 research outputs found
Bilateral Personalized Dialogue Generation with Dynamic Persona-Aware Fusion
Generating personalized responses is one of the major challenges in natural
human-robot interaction. Current researches in this field mainly focus on
generating responses consistent with the robot's pre-assigned persona, while
ignoring the user's persona. Such responses may be inappropriate or even
offensive, which may lead to the bad user experience. Therefore, we propose a
bilateral personalized dialogue generation (BPDG) method with dynamic
persona-aware fusion via multi-task transfer learning to generate responses
consistent with both personas. The proposed method aims to accomplish three
learning tasks: 1) an encoder is trained with dialogue utterances added with
corresponded personalized attributes and relative position (language model
task), 2) a dynamic persona-aware fusion module predicts the persona presence
to adaptively fuse the contextual and bilateral personas encodings (persona
prediction task) and 3) a decoder generates natural, fluent and personalized
responses (dialogue generation task). To make the generated responses more
personalized and bilateral persona-consistent, the Conditional Mutual
Information Maximum (CMIM) criterion is adopted to select the final response
from the generated candidates. The experimental results show that the proposed
method outperforms several state-of-the-art methods in terms of both automatic
and manual evaluations.Comment: 14 pages, 6 figure