Summary of A Regularization-based Transfer Learning Method For Information Extraction Via Instructed Graph Decoder, by Kedi Chen and Jie Zhou and Qin Chen and Shunyu Liu and Liang He
A Regularization-based Transfer Learning Method for Information Extraction via Instructed Graph Decoder
by Kedi Chen, Jie Zhou, Qin Chen, Shunyu Liu, Liang He
First submitted to arxiv on: 1 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 This paper proposes a novel transfer learning approach for information extraction (IE) tasks. The existing methods focus on training task-specific models, neglecting the common knowledge among different IE tasks. To address this issue, the authors introduce a regularization-based transfer learning method called TIE, which utilizes an instructed graph decoder to learn and transfer common knowledge across different IE tasks. The approach first constructs an instruction pool from well-known IE datasets and then decodes various complex structures into a graph uniformly based on corresponding instructions. Additionally, the authors develop a task-specific regularization strategy to alleviate the label inconsistency problem among various IE tasks. Experimental results on 12 datasets spanning four IE tasks demonstrate the effectiveness of the proposed method. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us learn more about how computers can understand and extract important information from texts. Right now, we have many different ways for computers to do this, but each way is specialized for a specific type of text or task. This makes it hard for computers to use what they’ve learned in one area and apply it to another. The researchers propose a new approach called TIE that allows computers to learn from many different types of texts at once, which makes them better at understanding any kind of text. They also show how their method can fix the problem where different tasks might have different answers for the same question. |
Keywords
* Artificial intelligence * Decoder * Regularization * Transfer learning
