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 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| 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 ywm18@mails.tsinghua.edu.cn, xuhua@mail.tsinghua.edu.cn, ziqiyuan@bupt.edu.cn, 1120171196@bit.edu.cn |
| 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. |