Summary of Rng: Reducing Multi-level Noise and Multi-grained Semantic Gap For Joint Multimodal Aspect-sentiment Analysis, by Yaxin Liu et al.
RNG: Reducing Multi-level Noise and Multi-grained Semantic Gap for Joint Multimodal Aspect-Sentiment Analysis
by Yaxin Liu, Yan Zhou, Ziming Li, Jinchuan Zhang, Yu Shang, Chenyang Zhang, Songlin Hu
First submitted to arxiv on: 20 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposed RNG framework addresses limitations in Joint Multimodal Aspect-Sentiment Analysis (JMASA), including instance- and feature-level noise, and coarse- and fine-grained semantic gaps. The framework uses three constraints: Global Relevance Constraint, Information Bottleneck Constraint, and Semantic Consistency Constraint to reduce these issues. Experimental results on two datasets demonstrate state-of-the-art performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Joint Multimodal Aspect-Sentiment Analysis (JMASA) is important because it helps understand how people feel about different things, like products or services. Right now, there are some problems with existing methods that make them not very good at getting the right answers. This new method, called RNG, tries to fix those problems by using special rules to help remove noise and find the right answers. So far, it seems to be doing a really good job! |
