Nice Perfume. How Long Did You Marinate in It? Multimodal Sarcasm Explanation
Authors: Poorav Desai, Tanmoy Chakraborty, Md Shad Akhtar10563-10571
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results demonstrate convincing results over various baselines (adopted for Mu SE) across five evaluation metrics. We also conduct human evaluation on predictions and obtain Fleiss Kappa score of 0.4 as a fair agreement among 25 evaluators. |
| Researcher Affiliation | Academia | Poorav Desai, Tanmoy Chakraborty, Md Shad Akhtar Indraprastha Institute of Information Technology, Delhi (IIIT Delhi), India {desai19010@iiitd.ac.in, tanmoy, shad.akhtar}@iiitd.ac.in |
| Pseudocode | No | The paper includes architectural diagrams (Figure 3) but no formal pseudocode or algorithm blocks. |
| Open Source Code | Yes | Reproducibility: The source code and dataset are available at https://github.com/LCS2-IIITD/Multimodal Sarcasm-Explanation-Mu SE. |
| Open Datasets | Yes | To address Mu SE, we curate MORE, a novel multimodal sarcasm explanation dataset, consisting of 3510 sarcastic posts with natural language explanations manually generated by expert annotators. |
| Dataset Splits | Yes | We perform experiments on MORE and use 85:5:10 split to create train (2983), validation (175), and test (352) sets. |
| Hardware Specification | No | The paper mentions training details like 'batch size = 16' and '125 epochs' but does not specify any particular GPU models, CPU models, or other hardware specifications. |
| Software Dependencies | No | The paper mentions 'BART (Lewis et al. 2020) tokenizer' and 'Adam W (Loshchilov and Hutter 2017) optimizer' but does not provide specific version numbers for these or other software libraries. |
| Experiment Setup | Yes | Hyperparameters: We employ BART (Lewis et al. 2020) tokenizer with maximum token length as 256. We use Adam W (Loshchilov and Hutter 2017) optimizer with learning rate of 1e 5 for the single cross-modal encoder and 3e 4 for the LM head of decoder. We train Ex More for 125 epochs with batch size = 16. During training, the cross-entropy loss is monitored over the validation set with image encoder in a frozen state. |