Personalized and Emotional Conversation

1 papers with code • 1 benchmarks • 1 datasets

Personalized and Emotional Conversation (PEC) is defined as follows: Given the personalized information ($P_{R1}$ and $P_{R2}$) of two speakers, their conversation context $C$, the emotion $E_K$ and DA $D_K$ of the response to be generated, and the personalized information $P_{K}$ of the responder, the goal is to generate an anthropomorphic response $Y$. \begin{equation} Y = argmax_{Y'}P(Y'|C, E_K, D_K, P_K) \label{task_definition} \end{equation}

Particularly, context $C={(U_1,E_1,D_1,P_1),\cdots,(U_{K-1},E_{K-1},D_{K-1},P_{K-1})}$ contains multi-turn conversation content (i.e., utterance $U_i$), emotion $E_i$ of the associated utterance, DA $D_i$ of the associated utterance, and personalized information $P_i$ of the associated speaker.


Most implemented papers

CPED: A Large-Scale Chinese Personalized and Emotional Dialogue Dataset for Conversational AI

scutcyr/CPED 29 May 2022

Finally, we provide baseline systems for these tasks and consider the function of speakers' personalities and emotions on conversation.