Summary of Llm-driven Multimodal Opinion Expression Identification, by Bonian Jia and Huiyao Chen and Yueheng Sun and Meishan Zhang and Min Zhang
LLM-Driven Multimodal Opinion Expression Identification
by Bonian Jia, Huiyao Chen, Yueheng Sun, Meishan Zhang, Min Zhang
First submitted to arxiv on: 26 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Sound (cs.SD); Audio and Speech Processing (eess.AS)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 study extends Opinion Expression Identification (OEI) in NLP to incorporate multimodal inputs, highlighting the importance of auditory cues in conveying emotional nuances beyond text-based expressions. The novel MOEI task integrates text and speech to mimic real-world scenarios. The researchers construct two datasets, CI-MOEI and CIM-OEI, using CMU MOSEI, IEMOCAP, and MPQA datasets. A template is designed for the OEI task to leverage the generative capabilities of large language models (LLMs). The proposed method, STOEI, combines speech and text modalities to identify opinion expressions, achieving significant performance improvements. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study helps us better understand how people express opinions in different ways. It’s like trying to figure out what someone means when they say something with a happy or sad tone of voice. The researchers created new datasets and a special way to use large language models to identify these emotional expressions. Their method is the best so far, improving performance by 9.20%. |
Keywords
» Artificial intelligence » Nlp
