2 papers
cs.CL2022
Graph-to-Text Generation with Dynamic Structure Pruning
Liang Li, Ruiying Geng, Bowen Li +4
Most graph-to-text works are built on the encoder-decoder framework with cross-attention mechanism. Recent studies have shown that explicitly modeling the input graph structure can…
cs.CL2020
Learning Better Representation for Tables by Self-Supervised Tasks
Liang Li, Can Ma, Yinliang Yue +2
Table-to-text generation aims at automatically generating natural text to help people to conveniently obtain the important information in tables. Although neural models for table-t…