  Data-to-text generation models face challenges in ensuring data fidelity by
referring to the correct input source. To inspire studies in this area, Wiseman
et al. (2017) introduced the RotoWire corpus on generating NBA game summaries
from the box- and line-score tables. However, limited attempts have been made
in this direction and the challenges remain. We observe a prominent bottleneck
in the corpus where only about 60% of the summary contents can be grounded to
the boxscore records. Such information deficiency tends to misguide a
conditioned language model to produce unconditioned random facts and thus leads
to factual hallucinations. In this work, we restore the information balance and
revamp this task to focus on fact-grounded data-to-text generation. We
introduce a purified and larger-scale dataset, RotoWire-FG (Fact-Grounding),
with 50% more data from the year 2017-19 and enriched input tables, hoping to
attract more research focuses in this direction. Moreover, we achieve improved
data fidelity over the state-of-the-art models by integrating a new form of
table reconstruction as an auxiliary task to boost the generation quality.
