Empathy and emotion prediction are key components in the development of effective and empathetic agents, amongst several other applications. The WASSA shared task on empathy and emotion prediction in interactions presents an opportunity to benchmark approaches to these tasks. Appropriately selecting and representing the historical context is crucial in the modelling of empathy and emotion in conversations. In our submissions, we model empathy, emotion polarity and emotion intensity of each utterance in a conversation by feeding the utterance to be classified together with its conversational context, i.e., a certain number of previous conversational turns, as input to an encoder Pre-trained Language Model, to which we append a regression head for prediction. We also model perceived counterparty empathy of each interlocutor by feeding all utterances from the conversation and a token identifying the interlocutor for which we are predicting the empathy. Our system officially ranked $1^{st}$ at the CONV-turn track and $2^{nd}$ at the CONV-dialog track.

在这篇研究论文中，我们通过将对话的发言与上下文输入到一个预训练的语言模型编码器，并附加一个回归头进行预测，来对每个发言进行情感、情绪极性和情绪强度的建模。此外，我们还通过将对话中的所有发言和一个标识相应发言者的标记输入进行预测，对各个参与者的对方人的共情进行建模。我们的系统在CONV-turn任务中排名第1，CONV-dialog任务中排名第2。

ConText at WASSA 2024 Empathy and Personality Shared Task:
  History-Dependent Embedding Utterance Representations for Empathy and Emotion
  Prediction in Conversations

WASSA 2024 共情和人格共享任务中的ConText：基于历史依赖的嵌入式话语表示对话中的共情和情感预测