Summary of Bvsp: Broad-view Soft Prompting For Few-shot Aspect Sentiment Quad Prediction, by Yinhao Bai et al.
BvSP: Broad-view Soft Prompting for Few-Shot Aspect Sentiment Quad Prediction
by Yinhao Bai, Yalan Xie, Xiaoyi Liu, Yuhua Zhao, Zhixin Han, Mengting Hu, Hang Gao, Renhong Cheng
First submitted to arxiv on: 11 Jun 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 ASQP task aims to predict four aspect-based elements: aspect term, opinion term, aspect category, and sentiment polarity. However, unseen aspects pose challenges for trained neural models. To address this, the paper formulates ASQP as a few-shot scenario, focusing on fast adaptation in real applications. A new dataset (FSQP) is constructed, which contains richer categories and is more balanced for few-shot studies. Additionally, recent methods extract quads using generation paradigms, but overlook correlations among templates. The proposed Broadview Soft Prompting (BvSP) method addresses this issue by selecting relevant templates with Jensen-Shannon divergence and introducing soft prompts to guide the pre-trained language model. Experimental results show that BvSP outperforms state-of-the-art methods under four few-shot settings and public datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper tries to make computers better at understanding what people think about certain things. It wants to figure out how to teach a computer to quickly learn about new topics by looking at only a little bit of information. Right now, computers are good at understanding some things, but they get confused when they encounter something new. The researchers created a special dataset that helps the computer learn faster and better. They also came up with a new way to use language models, which is called Broadview Soft Prompting (BvSP). This method helps the computer understand different perspectives by looking at multiple examples of how people express themselves. The results show that this new approach works really well! |
Keywords
» Artificial intelligence » Few shot » Language model » Prompting
