  In open domain table-to-text generation, we notice that the unfaithful
generation usually contains hallucinated content which can not be aligned to
any input table record. We thus try to evaluate the generation faithfulness
with two entity-centric metrics: table record coverage and the ratio of
hallucinated entities in text, both of which are shown to have strong agreement
with human judgements. Then based on these metrics, we quantitatively analyze
the correlation between training data quality and generation fidelity which
indicates the potential usage of entity information in faithful generation.
Motivated by these findings, we propose two methods for faithful generation: 1)
augmented training by incorporating the auxiliary entity information, including
both an augmented plan-based model and an unsupervised model and 2) training
instance selection based on faithfulness ranking. We show these approaches
improve generation fidelity in both full dataset setting and few shot learning
settings by both automatic and human evaluations.
