Reforming Emotion Recognition in Conversation with InstructERC Framework

Kernkonzepte

InstructERC proposes a generative framework for emotion recognition in conversation using Large Language Models (LLMs) and introduces novel emotional alignment tasks. The approach significantly outperforms previous models on three benchmarks.

Zusammenfassung

InstructERC introduces a new approach to emotion recognition in conversation, emphasizing generative paradigms and unified designs. The framework includes a retrieval template module, emotional alignment tasks, and achieves state-of-the-art results on commonly used datasets. Extensive analysis provides empirical guidance for practical applications.

The content discusses the importance of modeling emotional tendencies in conversations influenced by historical utterances and speaker perceptions. It compares different paradigms for emotion recognition based on LLMs, recurrent-based methods, and GNN-based methods. The study highlights the effectiveness of LLMs in natural language reasoning tasks.

The authors present an overview of the InstructERC framework, including the retrieval template module and emotional alignment tasks. They conduct experiments on standard benchmark datasets to evaluate the performance of InstructERC compared to baselines. The study also explores data scaling experiments on a unified dataset to demonstrate robustness and generalization capabilities.

Statistiken

Our LLM-based plugin framework significantly outperforms all previous models.
Achieves comprehensive SOTA on three commonly used ERC datasets.
IEMOCAP dataset: 108 conversations, 5163 utterances.
MELD dataset: 1038 conversations, 9989 utterances.
EmoryNLP dataset: 713 conversations, 9934 utterances.

Zitate

"The question is not whether intelligent machines can have emotions, but whether machines without emotions can achieve intelligence." - Minsky (1988)

Wichtige Erkenntnisse aus

InstructERC

by Shanglin Lei... um arxiv.org 03-13-2024

https://arxiv.org/pdf/2309.11911.pdf

Tiefere Fragen

How does InstructERC's approach to emotion recognition differ from traditional discriminative frameworks

InstructERC differs from traditional discriminative frameworks in its approach to emotion recognition by reformulating the task from a discriminative framework to a generative framework using Large Language Models (LLMs). In traditional discriminative frameworks, researchers typically fine-tune models with context-free utterances and extract feature vectors for downstream tasks. However, InstructERC introduces a retrieval template module that explicitly integrates multi-granularity dialogue supervision information. This allows the model to reason through instructions, historical content, label statements, and demonstration retrievals in a more holistic manner.

What are the implications of unifying emotion labels across benchmarks for real-world applications

The unification of emotion labels across benchmarks has significant implications for real-world applications. By aligning emotional labels across datasets, InstructERC creates a standardized set of emotional categories that can be applied consistently in various scenarios. This standardization enhances interoperability between different datasets and facilitates better comparison and evaluation of models trained on these datasets. Real-world applications stand to benefit from this unified labeling scheme as it enables more robust and generalizable emotion recognition systems that can perform effectively across diverse conversational contexts.

How might the integration of multimodal aspects enhance the performance of InstructERC in future research

Integrating multimodal aspects into InstructERC could significantly enhance its performance in future research. By incorporating additional modalities such as audio or visual cues alongside textual data, the model can capture richer contextual information related to emotions in conversations. Multimodal integration can provide complementary signals that improve the overall understanding of emotional nuances expressed by speakers. This enhanced comprehension can lead to more accurate emotion recognition results and enable the model to adapt better to complex conversational dynamics where emotions are conveyed through multiple channels simultaneously.