Summary of Unifying Structured Data As Graph For Data-to-text Pre-training, by Shujie Li et al.
Unifying Structured Data as Graph for Data-to-Text Pre-Training
by Shujie Li, Liang Li, Ruiying Geng, Min Yang, Binhua Li, Guanghu Yuan, Wanwei He, Shao Yuan, Can Ma, Fei Huang, Yongbin Li
First submitted to arxiv on: 2 Jan 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 This paper proposes a unified data-to-text (D2T) generation framework that can handle various types of structured data, including tables, key-value pairs, and knowledge graphs. The authors design a structure-enhanced pre-training method for D2T generation using a Transformer architecture. Specifically, they introduce a position matrix to capture relative positional information between connected nodes in the input graph and a new attention mechanism that incorporates explicit connectivity structures. Experimental results on six benchmark datasets demonstrate the effectiveness of their proposed model. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes it possible for computers to turn structured data into natural language text. They want to do this for many different types of data, like tables or lists, rather than just one type. To make this happen, they created a special way to train a computer program (called a Transformer) that takes advantage of the structure in the data. This helps the program generate more accurate and helpful text from the data. They tested their approach on many different datasets and found it works really well. |
Keywords
» Artificial intelligence » Attention » Transformer
