  Semantic parsers map natural language utterances to meaning representations.
The lack of a single standard for meaning representations led to the creation
of a plethora of semantic parsing datasets. To unify different datasets and
train a single model for them, we investigate the use of Multi-Task Learning
(MTL) architectures. We experiment with five datasets (Geoquery, NLMaps, TOP,
Overnight, AMR). We find that an MTL architecture that shares the entire
network across datasets yields competitive or better parsing accuracies than
the single-task baselines, while reducing the total number of parameters by
68%. We further provide evidence that MTL has also better compositional
generalization than single-task models. We also present a comparison of task
sampling methods and propose a competitive alternative to widespread
proportional sampling strategies.
