Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
CauAIN: Causal Aware Interaction Network for Emotion Recognition in Conversations
Authors: Weixiang Zhao, Yanyan Zhao, Xin Lu
IJCAI 2022 | Venue PDF | 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 EMAIL |
| 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. |