Advancing Emotion Classification with an LLM for Emotion-Cause Pair Extraction in Conversations

Core Concepts

A two-stage pipeline combining a fine-tuned LLM for emotion classification and a BiLSTM-based network for cause extraction achieves state-of-the-art performance on the SemEval-2024 Task 3 "The Competition of Multimodal Emotion Cause Analysis in Conversations".

Abstract

The paper presents a system developed by the PetKaz team for the SemEval-2024 Task 3 "The Competition of Multimodal Emotion Cause Analysis in Conversations". The task focuses on extracting emotion-cause pairs from dialogues. The proposed approach consists of two stages: Emotion classification: The authors fine-tune GPT-3.5 to classify utterances into one of the seven emotion categories (neutral, anger, disgust, fear, joy, sadness, surprise). The model considers both the target utterance and the preceding utterance to make the classification. Cause extraction: The authors use a BiLSTM-based network to detect the causal utterances for non-neutral utterances. The model takes into account the utterance embeddings, speaker information, and the emotion label to predict whether a previous utterance is the cause of the current emotional utterance. The authors score 2nd out of 15 teams in the Subtask 1 "Textual Emotion-Cause Pair Extraction in Conversations" based on the weighted-average proportional F1 score of 0.264, demonstrating the effectiveness of their approach. The paper also provides an extensive analysis of the model's performance. Key insights include: The emotion classifier struggles the most with correctly identifying disgust, likely due to the class imbalance in the dataset. The cause extractor performs better when the cause is closer to the emotional utterance. The authors observe instances where emotions appear before their causes, suggesting the need to revisit the definition of "cause" in dialogue contexts. Overall, the authors highlight the complexity of accurately identifying emotions and their causes in conversational data, and provide suggestions for future improvements, such as enhancing data annotation and speaker representations.

Stats

91% of emotions have corresponding causes, and one emotion may be triggered by multiple causes in different utterances. 16% of emotions cause several different emotions. The training set contains 1,236 dialogs with 12,346 utterances and 8,565 emotion-cause pairs. The development set contains 138 dialogs with 1,273 utterances and 799 emotion-cause pairs.

Quotes

"Recognizing the emotional implications of an utterance provides a deeper understanding of dialog, enabling the development of more human-like dialog systems." "We believe that this part of the task can be more accurately defined as a causal emotion entailment."

Key Insights Distilled From

PetKaz at SemEval-2024 Task 3

by Roman Kazako... at arxiv.org 04-09-2024

https://arxiv.org/pdf/2404.05502.pdf

Deeper Inquiries

How can the dataset annotation be improved to better capture the nuances of emotion-cause relationships in dialogues

To improve dataset annotation for capturing the nuances of emotion-cause relationships in dialogues, several strategies can be implemented. Firstly, annotators should undergo rigorous training to ensure a consistent understanding of what constitutes an emotion cause. Clear guidelines and examples should be provided to aid in the identification of causes within dialogues. Additionally, annotators could be encouraged to consider the context of the conversation as a whole rather than focusing solely on individual utterances. This broader perspective may help in identifying subtle cues and implicit causes that contribute to the emotional dynamics of the dialogue. Moreover, incorporating feedback loops and quality checks in the annotation process can help refine annotations over time, ensuring a higher level of accuracy and consistency in capturing emotion-cause relationships.

What other contextual information, beyond the preceding utterance, could be leveraged to enhance emotion classification performance

Beyond the preceding utterance, leveraging additional contextual information can significantly enhance emotion classification performance. One key aspect to consider is the speaker's emotional state and intentions throughout the conversation. By analyzing the speaker's emotional trajectory and patterns of expression, the model can gain a deeper understanding of the underlying emotions in each utterance. Furthermore, incorporating information about the conversational context, such as the overall tone of the dialogue, recurring themes, and the relationship between speakers, can provide valuable insights into the emotional dynamics at play. Utilizing sentiment analysis techniques to analyze sentiment shifts within the conversation can also aid in more accurate emotion classification. By integrating these contextual cues, the model can make more informed decisions about the emotional content of each utterance.

How can the cause extraction model be further improved to handle cases where emotions precede their causes or where multiple causes contribute to a single emotion

To enhance the cause extraction model for cases where emotions precede their causes or where multiple causes contribute to a single emotion, several approaches can be considered. Firstly, incorporating temporal dependencies into the model can help capture the sequence of events leading to an emotion. By analyzing the chronological order of utterances and their corresponding emotions, the model can better identify causal relationships, even when emotions precede their causes. Additionally, implementing a hierarchical approach that considers both local and global context within the conversation can help in identifying multiple causes contributing to a single emotion. This hierarchical model can analyze the conversation at different levels of granularity, from individual utterances to the overall dialogue structure, to extract complex causal relationships accurately. Furthermore, integrating attention mechanisms that focus on relevant parts of the conversation based on the emotional context can improve the model's ability to identify nuanced causes within dialogues. By combining these strategies, the cause extraction model can handle a wider range of scenarios and provide more comprehensive insights into emotion-cause relationships in conversations.