Mutual-Enhanced Incongruity Learning Network for Multi-Modal Sarcasm Detection
Authors: Yang Qiao, Liqiang Jing, Xuemeng Song, Xiaolin Chen, Lei Zhu, Liqiang Nie
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on a widely-used dataset demonstrate the superiority of our model over cutting-edge methods. |
| Researcher Affiliation | Academia | School of Computer Science and Technology, Shandong University, Qingdao, China 2School of Software, Shandong University, Jinan, China 3School of Information Science and Engineering, Shandong Normal University, Jinan, China 4School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen campus), Shenzhen, China |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | We released the codes and parameters to facilitate the research community1. 1https://frd1228.wixsite.com/milnet. |
| Open Datasets | Yes | Following previous works, we evaluated our model on a public available multi-modal sarcasm detection dataset (Cai, Cai, and Wan 2019) with English tweets. |
| Dataset Splits | Yes | Furthermore, the dataset is divided into a training set, a validating set, and a testing set, which includes 19, 816, 2, 410, and 2, 409 samples, respectively. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. It only refers to supplementary material for implementation details. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. It refers to supplementary material for implementation details. |
| Experiment Setup | No | The paper mentions loss functions (cross-entropy loss with Frobenius norm regularization) but does not provide specific hyperparameter values (e.g., learning rate, batch size, number of epochs) or other detailed training configurations in the main text. It refers to supplementary material for implementation details. |