UIR - ISC at S em E val-2024 Task 3: Textual Emotion-Cause Pair Extraction in Conversations (2024)

Abstract: The goal of Emotion Cause Pair Extraction (ECPE) is to explore the causes of emotion changes and what causes a certain emotion. This paper proposes a three-step learning approach for the task of Textual Emotion-Cause Pair Extraction in Conversations in SemEval-2024 Task 3, named ECSP. We firstly perform data preprocessing operations on the original dataset to construct negative samples. Secondly, we use a pre-trained model to construct token sequence representations with contextual information to obtain emotion prediction. Thirdly, we regard the textual emotion-cause pair extraction task as a machine reading comprehension task, and fine-tune two pre-trained models, RoBERTa and SpanBERT. Our results have achieved good results in the official rankings, ranking 3rd under the strict match with the Strict F1-score of 15.18%, which further shows that our system has a robust performance.

## introduction emotions are innate to humans and significantly affect people's social interactions, decision-making, and cognition. people are becoming more interested in developing human-like reactions as social media evolves. therefore, the recognition of emotions in the text is an important topic in natural language processing and its applications @xcite . in addition to emotion recognition, the research on the cause behind emotions in conversation scenarios is more complex, such as customer support, mental health care, human-computer interaction, etc @xcite . thus, it is important to recognize the potential cause behind an individual's emotional state, i.e., emotion cause analysis (eca). 1 in recent research, @xcite proposed the emotion cause pair extraction (ecpe) * corresponding author 1 description of the task by the organizer of semeval-2024 task 3 task, which is used to automatically predict emotions in documents and recognize the corresponding causes of those emotions. this task has attracted attention from a number of academics @xcite @xcite . however, the ecpe task studies the emotion-cause relationship of specific events in the document, while in the conversational scene, due to the interaction of multiple speakers, the dialogue contains more diverse and richer emotional expressions, which makes the conversation continue to advance as the conversation progresses. emotions are also constantly changing, and the emotion of one utterance may be caused by multiple utterances. in this paper, we propose a three-step learning approach, emotion-cause-span pair extraction in conversation (ecsp), for subtask 1 of semeval-2024 task 3: textual emotion-cause pair extraction in conversations. ecsp consists of three modules: the data preprocessing module, the emotion classification module, and the textual emotioncause pair extraction module. we first preprocessed the dataset to obtain a large number of negative examples. then, the pre-trained model bert is used to construct token sequence representations with contextual information that are fed into a feedforward neural network layer for emotion prediction. in the textual emotion-cause pair extraction module, in order to obtain causal span, we finetuned pre-trained models such as roberta and spanbert to make it a machine reading comprehension (mrc) task @xcite . in the official ranking, our team ranked 3rd under the strict match with the strict f1-score of 15.18%, and ranked 7th under the proportional match with the proportional f1-score of 19.63%. 2 background ## system overview in order to implement the task of textual emotioncause pair extraction in conversations, we have designed the ecsp approach, which contains three main modules, namely data preprocessing, emotion classification, and textual emotion-cause pair extraction. firstly, in the data preprocessing module, the dataset is preprocessed to obtain a large number of negative samples. then the pre-trained model bert is used to convert token sequences with contextual information in the conversation into semantic representations and predict emotions in the emotion classification module. finally, textual emotioncause pairs are extracted based on the predicted emotions in the textual emotion-cause pair extraction module. the overall architecture of ecsp system is shown in figure 2 , and the detailed description for each part is presented as follows. ## conclusion in this paper, we introduce the system implementation of semeval-2024 task 3: textual emotion-cause pair extraction in conversations. we propose an integrated system named emotion-cause-span pair extraction in conversation (ecsp), which was implemented in three modules: preprocessing data, emotion classification with contextual information input, and textual emotion-cause pair extraction, and it performed well in the official rankings. in the future, we will utilize this dataset to investigate if the speaker attribute affects the extraction task of emotion-cause pairs, as well as to implement methods such as external knowledge bases to improve our system's recognition performance on ecpe tasks.