The WebNLG (Gardent et al., 2017) dataset con-
sists of 22K examples, and the input x is a sequence
of (subject, property, object) triples. The average
output length is 22.5. In the training and validation
splits, the input describes entities from 9 distinct
DBpedia categories (e.g., Monument). The test
split consists of two parts: the ﬁrst half contains
DB categories seen in training data, and the sec-
ond half contains 5 unseen categories. These un-
seen categories are used to evaluate extrapolation.
We use the ofﬁcial evaluation script, which reports
BLEU, METEOR and TER (Snover et al., 2006).
DART (Radev et al., 2020) is an open domain
table-to-text dataset, with similar input format
(entity-relation-entity triples) as WebNLG. The av-
erage output length is 21.6. It consists of 82K ex-
amples from WikiSQL, WikiTableQuestions, E2E,
and WebNLG and applies some manual or auto-
mated conversion. We use the ofﬁcial evaluation
script13 and report BLEU, METEOR, TER, Mover-
Prefix-Tuning