ConText 在 WASSA 2024 共情与个性共享任务中的应用：用于对话中共情与情绪预测的历史依赖嵌入话语表示
ConText at WASSA 2024 Empathy and Personality Shared Task: History-Dependent Embedding Utterance Representations for Empathy and Emotion Prediction in Conversations
AI总结：
本文提出利用历史对话上下文与预训练语言模型相结合的话语表示方法，在 WASSA 2024 共情与情绪预测任务中分别取得 CONV-turn 第一和 CONV-dialog 第二的成绩。
摘要：
共情与情绪预测是开发有效且具有共情能力的智能体以及其他多种应用中的关键组成部分。WASSA 关于交互中共情与情绪预测的共享任务为对这些任务的方法进行基准测试提供了机会。在对话中建模共情与情绪时，恰当地选择并表示历史上下文至关重要。在我们的提交中，我们通过将要分类的话语连同其对话上下文（即一定数量的先前对话轮次）一起作为输入馈送到编码器预训练语言模型中，并在其上附加一个回归头进行预测，从而对对话中每个话语的共情、情绪极性和情绪强度进行建模。我们还通过馈送对话中的所有话语以及一个标识我们所预测共情对象的对话者的标记，来对每个对话者的感知对方共情进行建模。我们的系统在 CONV-turn 赛道中官方排名第一，在 CONV-dialog 赛道中官方排名第二。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
