Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Learning Modality-Specific Representations with Self-Supervised Multi-Task Learning for Multimodal Sentiment Analysis
Authors: Wenmeng Yu, Hua Xu, Ziqi Yuan, Jiele Wu10790-10797
AAAI 2021 | Venue PDF | LLM Run Details | Input Tokens: 16,145 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,063 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Last, we conduct extensive experiments on three public multimodal baseline datasets. The experimental results validate the reliability and stability of auto-generated unimodal supervisions. On MOSI and MOSEI datasets, our method surpasses the current state-of-the-art methods. |
| Researcher Affiliation | Academia | Wenmeng Yu, Hua Xu, Ziqi Yuan, Jiele Wu State Key Laboratory of Intelligent Technology and Systems, Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Unimodal Supervisions Update Policy |
| Open Source Code | Yes | The full codes are available at https://github.com/thuiar/Self-MM. |
| Open Datasets | Yes | In this work, we use three public multimodal sentiment analysis datasets, MOSI (Zadeh et al. 2016), MOSEI (Zadeh et al. 2018b), and SIMS (Yu et al. 2020a). |
| Dataset Splits | Yes | Table 2: Dataset statistics in MOSI, MOSEI, and SIMS. # Train # Valid # Test # All |
| Hardware Specification | No | The paper mentions experimental settings like optimizer and learning rates, but it does not specify any hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using a 'pre-trained 12-layers BERT' and 's LSTM' but does not provide specific version numbers for any software dependencies or libraries like PyTorch, TensorFlow, etc. |
| Experiment Setup | Yes | We use Adam as the optimizer and use the initial learning rate of 5e 5 for Bert and 1e 3 for other parameters. |