INFINITY: A Simple Yet Effective Unsupervised Framework for Graph-Text Mutual Conversion. (arXiv:2209.10754v1 [cs.CL])

Graph-to-text (G2T) generation and text-to-graph (T2G) triple extraction are
two essential tasks for constructing and applying knowledge graphs. Existing
unsupervised approaches turn out to be suitable candidates for jointly learning
the two tasks due to their avoidance of using graph-text parallel data.
However, they are composed of multiple modules and still require both entity
information and relation type in the training process. To this end, we propose
INFINITY, a simple yet effective unsupervised approach that does not require
external annotation …

arxiv conversion framework graph text unsupervised