Enhancing Large Language Model with Decomposed Reasoning for Emotion
  Cause Pair Extraction
Jialiang Wu Yi Shen Ziheng Zhang Longjun Cai
Emotion-Cause Pair Extraction (ECPE) involves extracting clause pairs
representing emotions and their causes in a document. Existing methods tend to
overfit spurious correlations, such as positional bias in existing benchmark
datasets, rather than capturing semantic features. Inspired by recent work, we
explore leveraging large language model (LLM) to address ECPE task without
additional training. Despite strong capabilities, LLMs suffer from
uncontrollable outputs, resulting in mediocre performance. To address this, we
introduce chain-of-thought to mimic human cognitive process and propose the
Decomposed Emotion-Cause Chain (DECC) framework. Combining inducing inference
and logical pruning, DECC guides LLMs to tackle ECPE task. We further enhance
the framework by incorporating in-context learning. Experiment results
demonstrate the strength of DECC compared to state-of-the-art supervised
fine-tuning methods. Finally, we analyze the effectiveness of each component
and the robustness of the method in various scenarios, including different LLM
bases, rebalanced datasets, and multi-pair extraction.