6.4 Extrapolation
We now investigate extrapolation performance to
unseen topics for both table-to-text and summariza-
tion. In order to construct an extrapolation setting,
we split the existing datasets so that training and
test cover different topics. For table-to-text, the
WebNLG dataset is labeled with table topics. There
are 9 categories that appear in training and dev, de-
noted as SEEN and 5 categories that only appear at
test time, denoted as UNSEEN. So we evaluate ex-
trapolation by training on the SEEN categories and
testing on the UNSEEN categories. For summa-
rization, we construct two extrapolation data splits:
9We also sample a dev split (with dev size = 30% × train-
ing size) for each training set. We use the dev split to choose
hyperparameters and perform early stopping.

10The number in the parenthesis refers to the training size.

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DART
Prefix-Tuning