Summary of Panosent: a Panoptic Sextuple Extraction Benchmark For Multimodal Conversational Aspect-based Sentiment Analysis, by Meng Luo et al.
PanoSent: A Panoptic Sextuple Extraction Benchmark for Multimodal Conversational Aspect-based Sentiment Analysis
by Meng Luo, Hao Fei, Bobo Li, Shengqiong Wu, Qian Liu, Soujanya Poria, Erik Cambria, Mong-Li Lee, Wynne Hsu
First submitted to arxiv on: 18 Aug 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 paper introduces a multimodal conversational Aspect-based Sentiment Analysis (ABSA) system that addresses gaps in existing ABSA research. It proposes two novel subtasks: Panoptic Sentiment Sextuple Extraction and Sentiment Flipping Analysis. The system is benchmarked using the PanoSent dataset, which features high-quality annotations for multimodality, multilingualism, and multi-scenarios. To address the tasks, the paper develops a Chain-of-Sentiment reasoning framework, a novel multimodal large language model (Sentica), and a paraphrase-based verification mechanism. The evaluations demonstrate the superiority of the proposed methods over strong baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper improves Aspect-based Sentiment Analysis by introducing a new approach that considers multiple modes, conversations, and sentiment changes. It creates a dataset called PanoSent with many examples to test its ideas. To analyze these examples, it uses special techniques like reasoning chains and language models. The results show that this approach is better than previous methods. |
Keywords
» Artificial intelligence » Large language model
