3260 papers • 126 benchmarks • 313 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.
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This work proposes CPED, a large-scale Chinese personalized and emotional dialogue dataset, which consists of multi-source knowledge related to empathy and personal characteristic, to be widely adopted by the NLP community as a new open benchmark for conversational AI research.
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