Summary of An Effective Incorporating Heterogeneous Knowledge Curriculum Learning For Sequence Labeling, by Xuemei Tang and Qi Su
An Effective Incorporating Heterogeneous Knowledge Curriculum Learning for Sequence Labeling
by Xuemei Tang, Qi Su
First submitted to arxiv on: 21 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 A novel framework for sequence labeling models is proposed to address the challenge of incorporating external knowledge while reducing training complexity. The two-stage curriculum learning (TCL) framework gradually introduces data instances from easy to hard, improving both performance and training speed. This approach is demonstrated to be effective on six Chinese word segmentation and Part-of-speech tagging datasets, enhancing the overall performance of sequence labeling models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers has created a new way for computers to learn about language. They wanted to make it easier for machines to understand text by using information from outside sources. However, this made things more complicated for the computer, taking longer and requiring more resources. To solve this problem, they developed a special system that helps train these models more efficiently. This system is called two-stage curriculum learning (TCL). It’s like teaching a student in stages, starting with easy material and gradually moving to harder topics. The researchers tested their approach on several datasets and found it improved the performance of language models. |
Keywords
* Artificial intelligence * Curriculum learning
