CauAIN: Causal Aware Interaction Network for Emotion Recognition in Conversations
Authors: Weixiang Zhao, Yanyan Zhao, Xin Lu
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on three benchmark datasets show that our model achieves better performance over most baseline models. |
| Researcher Affiliation | Academia | Weixiang Zhao , Yanyan Zhao , Xin Lu Harbin Institute of Technology, China {wxzhao, yyzhao, xlu}@ir.hit.edu.cn |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not include an unambiguous statement that the authors are releasing the code for the work described, nor does it provide a direct link to a source-code repository. |
| Open Datasets | Yes | We conduct experiments on three benchmark datasets from IEMOCAP [Busso et al., 2008], Daily Dialog [Li et al., 2017] and MELD [Poria et al., 2019b]. |
| Dataset Splits | Yes | Dataset Dialogues Train Val Test Train Val Test IEMOCAP 120 31 5,810 1,623 Daily Dialog 11,118 1,000 1,000 87,170 8,069 7,740 MELD 1,039 114 280 9,989 1,109 2,610 |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions 'RoBERTa Large model' and 'Adam optimizer' but does not provide specific version numbers for software dependencies or libraries. |
| Experiment Setup | Yes | For utterance-level feature extraction, we fine-tune Ro BERTa Large model for a batch size of 32 and Adam optimizer is adopted with learning rate of 1e-5. Thus, the dimension of utterance-level feature vector dm is 1024. For all representations in the following parts of Cau AIN, dh is set to 300. We train Cau AIN with Adam optimizer in a learning rate of 1e-4. |