In the realm of human-AI dialogue, the facilitation of empathetic responses
is important. Validation is one of the key communication techniques in
psychology, which entails recognizing, understanding, and acknowledging others'
emotional states, thoughts, and actions. This study introduces the first
framework designed to engender empathetic dialogue with validating responses.
Our approach incorporates a tripartite module system: 1) validation timing
detection, 2) users' emotional state identification, and 3) validating response
generation. Utilizing Japanese EmpatheticDialogues dataset - a textual-based
dialogue dataset consisting of 8 emotional categories from Plutchik's wheel of
emotions - the Task Adaptive Pre-Training (TAPT) BERT-based model outperforms
both random baseline and the ChatGPT performance, in term of F1-score, in all
modules. Further validation of our model's efficacy is confirmed in its
application to the TUT Emotional Storytelling Corpus (TESC), a speech-based
dialogue dataset, by surpassing both random baseline and the ChatGPT. This
consistent performance across both textual and speech-based dialogues
underscores the effectiveness of our framework in fostering empathetic human-AI
communication.

利用三部分模块系统，包括验证时机检测、用户情绪状态识别和验证式回应生成，本研究介绍了首个旨在促进共情对话的框架。通过利用日本 EmpatheticDialogues 数据集和 Task Adaptive Pre-Training（TAPT）基于 BERT 的模型，在所有模块的 F1 分数方面，表现超过随机基准线和 ChatGPT；在 TUT Emotional Storytelling Corpus（TESC）语音对话数据集上的应用也证实了模型的有效性，超过了随机基准线和 ChatGPT，强调了我们框架在促进人工智能与人类之间的共情沟通上的有效性。

情感状态认知：为共情对话生成验证性回应

Acknowledgment of Emotional States: Generating Validating Responses for  Empathetic Dialogue

Empathetic dialogue is an indispensable part of building harmonious social
relationships and contributes to the development of a helpful AI. Previous
approaches are mainly based on fine small-scale language models. With the
advent of ChatGPT, the application effect of large language models (LLMs) in
this field has attracted great attention. This work empirically investigates
the performance of LLMs in generating empathetic responses and proposes three
improvement methods of semantically similar in-context learning, two-stage
interactive generation, and combination with the knowledge base. Extensive
experiments show that LLMs can significantly benefit from our proposed methods
and is able to achieve state-of-the-art performance in both automatic and human
evaluations. Additionally, we explore the possibility of GPT-4 simulating human
evaluators.

大型语言模型在培养有同理心的对话、构建和谐社会关系以及发展有帮助的人工智能方面具有至关重要的作用。本研究通过实证调查了大型语言模型在生成有同理心的回应方面的性能，并提出了三种改进方法：语义上下文学习、两阶段交互生成和与知识库的结合。广泛的实验证明，我们的方法能够显著改善大型语言模型的性能，在自动评估和人工评估方面达到最先进的水平。此外，我们还探索了 GPT-4 模型模拟人工评估者的可能性。

利用大型语言模型的潜力进行共情回应的生成：实证研究和改进

Harnessing the Power of Large Language Models for Empathetic Response  Generation: Empirical Investigations and Improvements

Empathetic dialogue is a human-like behavior that requires the perception of
both affective factors (e.g., emotion status) and cognitive factors (e.g.,
cause of the emotion). Besides concerning emotion status in early work, the
latest approaches study emotion causes in empathetic dialogue. These approaches
focus on understanding and duplicating emotion causes in the context to show
empathy for the speaker. However, instead of only repeating the contextual
causes, the real empathic response often demonstrate a logical and
emotion-centered transition from the causes in the context to those in the
responses. In this work, we propose an emotion cause transition graph to
explicitly model the natural transition of emotion causes between two adjacent
turns in empathetic dialogue. With this graph, the concept words of the emotion
causes in the next turn can be predicted and used by a specifically designed
concept-aware decoder to generate the empathic response. Automatic and human
experimental results on the benchmark dataset demonstrate that our method
produces more empathetic, coherent, informative, and specific responses than
existing models.

本文提出一种情感原因转换图用于预测情感对话中的概念词，并设计了概念感知解码器以生成更具共情、连贯、信息丰富且特定的响应，并在基准数据集上取得了较好的结果。