Summary of Towards Robust Multimodal Sentiment Analysis with Incomplete Data, by Haoyu Zhang et al.
Towards Robust Multimodal Sentiment Analysis with Incomplete Data
by Haoyu Zhang, Wenbin Wang, Tianshu Yu
First submitted to arxiv on: 30 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Multimedia (cs.MM)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper addresses a recent issue in Multimodal Sentiment Analysis (MSA) where data incompleteness is a problem. The language modality typically contains dense sentiment information, so the authors propose an innovative Language-dominated Noise-resistant Learning Network (LNLN) to achieve robust MSA. The LNLN features two key modules: dominant modality correction (DMC) and dominant modality based multimodal learning (DMML). These modules enhance the model’s robustness across various noise scenarios by ensuring the quality of dominant modality representations. The authors conduct comprehensive experiments on several popular datasets, including MOSI, MOSEI, and SIMS, under random data missing scenarios. Their results show that LNLN consistently outperforms existing baselines in these challenging evaluation metrics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to fix a problem in analyzing emotions from multiple sources of information. When some of this information is missing, it’s hard to get accurate results. The authors created a new way to learn about emotions called Language-dominated Noise-resistant Learning Network (LNLN). It has two important parts that help the model be better at handling missing data. They tested LNLN on several datasets and found that it worked better than other methods in many cases. |
