In human-computer interaction, it is crucial for agents to respond to human
by understanding their emotions. Unraveling the causes of emotions is more
challenging. A new task named Multimodal Emotion-Cause Pair Extraction in
Conversations is responsible for recognizing emotion and identifying causal
expressions. In this study, we propose a multi-stage framework to generate
emotion and extract the emotion causal pairs given the target emotion. In the
first stage, Llama-2-based InstructERC is utilized to extract the emotion
category of each utterance in a conversation. After emotion recognition, a
two-stream attention model is employed to extract the emotion causal pairs
given the target emotion for subtask 2 while MuTEC is employed to extract
causal span for subtask 1. Our approach achieved first place for both of the
two subtasks in the competition.

本研究提出了一个多阶段框架，旨在通过目标情绪生成情绪并提取情绪的因果对。我们的方法在竞赛中在两个子任务中均获得第一名。

三星中国研究院 - 北京在 SemEval-2024 任务 3 中的多阶段对话情感原因匹配系统

Samsung Research China-Beijing at SemEval-2024 Task 3: A multi-stage  framework for Emotion-Cause Pair Extraction in Conversations

Causal Emotion Entailment (CEE) aims to discover the potential causes behind
an emotion in a conversational utterance. Previous works formalize CEE as
independent utterance pair classification problems, with emotion and speaker
information neglected. From a new perspective, this paper considers CEE in a
joint framework. We classify multiple utterances synchronously to capture the
correlations between utterances in a global view and propose a Two-Stream
Attention Model (TSAM) to effectively model the speaker's emotional influences
in the conversational history. Specifically, the TSAM comprises three modules:
Emotion Attention Network (EAN), Speaker Attention Network (SAN), and
interaction module. The EAN and SAN incorporate emotion and speaker information
in parallel, and the subsequent interaction module effectively interchanges
relevant information between the EAN and SAN via a mutual BiAffine
transformation. Extensive experimental results demonstrate that our model
achieves new State-Of-The-Art (SOTA) performance and outperforms baselines
remarkably.

本文提出了一种基于多个话语的联合分类方法，使用两个注意力网络与相互交互的模块来捕捉说话人的情感影响，从而实现对话历史中情感原因的发现，实验结果表明该模型具有最先进的性能并优于基线模型。

TSAM: 一种用于因果情感蕴涵的双流注意力模型

TSAM: A Two-Stream Attention Model for Causal Emotion Entailment

Existing document-level neural machine translation (NMT) models have
sufficiently explored different context settings to provide guidance for target
generation. However, little attention is paid to inaugurate more diverse
context for abundant context information. In this paper, we propose a Selective
Memory-augmented Neural Document Translation model to deal with documents
containing large hypothesis space of the context. Specifically, we retrieve
similar bilingual sentence pairs from the training corpus to augment global
context and then extend the two-stream attention model with selective mechanism
to capture local context and diverse global contexts. This unified approach
allows our model to be trained elegantly on three publicly document-level
machine translation datasets and significantly outperforms previous
document-level NMT models.

本文提出了一种选择性存储增强神经文档翻译模型，通过从训练语料库中检索相似的双语句对来增强全局上下文信息，并扩展双流注意力模型以捕捉局部上下文和多样化的全局上下文，该统一方法使我们的模型可以优雅地在三个公开的文档级机器翻译数据集上训练，并显著优于以前的文档级 NMT 模型。