Summary of On the Use Of Silver Standard Data For Zero-shot Classification Tasks in Information Extraction, by Jianwei Wang et al.
On the use of Silver Standard Data for Zero-shot Classification Tasks in Information Extraction
by Jianwei Wang, Tianyin Wang, Ziqian Zeng
First submitted to arxiv on: 28 Feb 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 proposes a new framework called Clean-LaVe that utilizes silver standard data to enhance the performance of zero-shot classification methods in information extraction (IE). By leveraging off-the-shelf models from other NLP tasks, the method converts IE into these tasks and uses them for inference without requiring large amounts of annotation data. The framework consists of four phases: obtaining silver data, identifying relatively clean data, fine-tuning the model using clean data, and making predictions on test data. Experimental results show that Clean-LaVe outperforms the baseline by 3-8% on various datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper develops a new method called Clean-LaVe to improve zero-shot classification in information extraction. It uses existing models from other areas of natural language processing (NLP) to help classify information without needing lots of training data. The approach has four steps: get the “silver” labeled data, find clean parts of it, train the model on that clean part, and then use it to make predictions. This method performs better than others by a small amount. |
Keywords
» Artificial intelligence » Classification » Fine tuning » Inference » Natural language processing » Nlp » Zero shot
