  In this work we study user controlled table-to-text generation where users
explore the content in a table by selecting cells and reading a natural
language description thereof automatically produce by a natural language
generator. Such generation models usually learn from carefully selected cell
combinations (clean cell selections); however, in practice users may select
unexpected, redundant, or incoherent cell combinations (noisy cell selections).
In experiments, we find that models perform well on test sets coming from the
same distribution as the train data but their performance drops when evaluated
on realistic noisy user inputs. We propose a fine-tuning regime with additional
user-simulated noisy cell selections. Models fine-tuned with the proposed
regime gain 4.85 BLEU points on user noisy test cases and 1.4 on clean test
cases; and achieve comparable state-of-the-art performance on the ToTTo
dataset.
