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..
Debiasing Multimodal Sarcasm Detection with Contrastive Learning
Authors: Mengzhao Jia, Can Xie, Liqiang Jing
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments show the superiority of the proposed framework. We conducted experiments on a publicly available multimodal sarcasm detection benchmark dataset (Cai, Cai, and Wan 2019). |
| Researcher Affiliation | Academia | Mengzhao Jia1, Can Xie1, Liqiang Jing2* 1Shandong University, 2University of Texas at Dallas |
| Pseudocode | No | The paper does not contain a pseudocode block or a clearly labeled algorithm block. |
| Open Source Code | Yes | As a byproduct, we have released the source code and the constructed dataset4. 4https://sharecode.wixsite.com/dmsd-cl. |
| Open Datasets | Yes | We conducted experiments on a publicly available multimodal sarcasm detection benchmark dataset (Cai, Cai, and Wan 2019). The dataset comprises 24,635 samples, each comprising textual content, an associated image, and a corresponding label. As a byproduct, we have released the source code and the constructed dataset4. 4https://sharecode.wixsite.com/dmsd-cl. |
| Dataset Splits | Yes | Following the original setting, the sizes of the training set, development set, and testing set are 19,815, 2,410, and 2,409, respectively. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., GPU models, CPU types) used for running the experiments, only mentioning pre-trained models and APIs like RoBERTa, ViT, and gpt3.5-turbo. |
| Software Dependencies | No | The paper mentions using 'RoBERTa-base', 'ViT', and 'gpt3.5-turbo' for various tasks, but does not provide specific version numbers for these software dependencies or other libraries. |
| Experiment Setup | Yes | Meanwhile, the hyperparameters K, τ, and λ are configured to 4, 0.07, and 0.9, respectively. We used the Adam optimizer to optimize our model with a learning rate of 1e 5. The mini-batch size is set to 16 and the maximum number of epochs for training is set to 20. |