This report presents a study on the emotional dialogue capability of ChatGPT,
an advanced language model developed by OpenAI. The study evaluates the
performance of ChatGPT on emotional dialogue understanding and generation
through a series of experiments on several downstream tasks. Our findings
indicate that while ChatGPT's performance on emotional dialogue understanding
may still lag behind that of supervised models, it exhibits promising results
in generating emotional responses. Furthermore, the study suggests potential
avenues for future research directions.

本研究基于由 OpenAI 研发的先进语言模型 ChatGPT，探讨了其情感对话能力。研究通过一系列下游任务实验，评估了 ChatGPT 在情感对话理解和生成方面的表现，并发现虽然 ChatGPT 在情感对话理解方面的性能仍然存在问题，但在生成情感回应方面表现良好。此外，本研究还提出了未来研究方向的可能性。

ChatGPT 是否具备情感对话能力？

Is ChatGPT Equipped with Emotional Dialogue Capabilities?

Towards human-like dialogue systems, current emotional dialogue approaches
jointly model emotion and semantics with a unified neural network. This
strategy tends to generate safe responses due to the mutual restriction between
emotion and semantics, and requires rare emotion-annotated large-scale dialogue
corpus. Inspired by the "think twice" behavior in human dialogue, we propose a
two-stage conversational agent for the generation of emotional dialogue.
Firstly, a dialogue model trained without the emotion-annotated dialogue corpus
generates a prototype response that meets the contextual semantics. Secondly,
the first-stage prototype is modified by a controllable emotion refiner with
the empathy hypothesis. Experimental results on the DailyDialog and
EmpatheticDialogues datasets demonstrate that the proposed conversational
outperforms the comparison models in emotion generation and maintains the
semantic performance in automatic and human evaluations.

本文提出了一种基于神经网络的两阶段对话模型，通过从语义和情感角度对话生成，不需要大量情感标注数据。实验表明，该模型在情感生成方面优于其他比较模型，并在语义性能上保持一致性。