  Traditional table-to-text natural language generation (NLG) tasks focus on
generating text from schemas that are already seen in the training set. This
limitation curbs their generalizabilities towards real-world scenarios, where
the schemas of input tables are potentially infinite. In this paper, we propose
the new task of table-to-text NLG with unseen schemas, which specifically aims
to test the generalization of NLG for input tables with attribute types that
never appear during training. To do this, we construct a new benchmark dataset
for this task. To deal with the problem of unseen attribute types, we propose a
new model that first aligns unseen table schemas to seen ones, and then
generates text with updated table representations. Experimental evaluation on
the new benchmark demonstrates that our model outperforms baseline methods by a
large margin. In addition, comparison with standard data-to-text settings shows
the challenges and uniqueness of our proposed task.
