  Data-to-text generation focuses on generating fluent natural language
responses from structured meaning representations (MRs). Such representations
are compositional and it is costly to collect responses for all possible
combinations of atomic meaning schemata, thereby necessitating few-shot
generalization to novel MRs. In this work, we systematically study the
compositional generalization of the state-of-the-art T5 models in few-shot
data-to-text tasks. We show that T5 models fail to generalize to unseen MRs,
and we propose a template-based input representation that considerably improves
the model's generalization capability. To further improve the model's
performance, we propose an approach based on self-training using fine-tuned
BLEURT for pseudo response selection. On the commonly-used SGD and Weather
benchmarks, the proposed self-training approach improves tree accuracy by 46%+
and reduces the slot error rates by 73%+ over the strong T5 baselines in
few-shot settings.
