ECR-Chain: Advancing Generative Language Models to Better Emotion-Cause Reasoners through Reasoning Chains
Authors: Zhaopei Huang, Jinming Zhao, Qin Jin
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct experiments on the RECCON-DD dataset [Poria et al., 2021]. This dataset supplements causal utterance annotations for each non-neutral utterance in the conversations of the Daily Dialog dataset [Li et al., 2017]. Extensive experimental results over various settings demonstrate the effectiveness of our method for predicting emotion-cause utterances and performing explainable emotion-cause reasoning. |
| Researcher Affiliation | Academia | Zhaopei Huang1 , Jinming Zhao2 , Qin Jin1 1Renmin University of China 2Independent Researcher {huangzhaopei, qjin}@ruc.edu.cn, zhaojinming1@gmail.com |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. It uses diagrams and textual descriptions to explain procedures. |
| Open Source Code | Yes | Our code, data and more details are at https://github.com/hzp3517/ECR-Chain. |
| Open Datasets | Yes | We conduct experiments on the RECCON-DD dataset [Poria et al., 2021]. This dataset supplements causal utterance annotations for each non-neutral utterance in the conversations of the Daily Dialog dataset [Li et al., 2017]. |
| Dataset Splits | Yes | Table 1: Dataset statistics. We consider each target utterance as a sample. A conversation may contain several target utterances, forming several samples. Statistics Train Valid Test Samples 4,562 200 1,099 |
| Hardware Specification | No | The paper mentions using 'Chat GPT (gpt-3.5-turbo-0613)' and 'Vicuna-7B-v1.3' but does not provide specific hardware details such as GPU models, CPU types, or memory used for running the experiments. |
| Software Dependencies | Yes | We utilize Chat GPT (gpt-3.5-turbo-0613) as our LLM... For the smaller language model, we opt for Vicuna-7B-v1.3... applied Lo RA fine-tuning [Hu et al., 2021] |
| Experiment Setup | Yes | Our total training batch is set to 256 (with gradient accumulation) and the learning rate is set to 1e-3. We train 10 epochs and pick the model that performed best on the validation set to evaluate on the test set. |