text: 437,140,1435567098739269635,322636963,Jonathan Berant,"Another piece of evidence in the quest for compositional generalization: New paper by Inbar Oren and w/ @jonherzig <LINK> (to appear in #emnlp2021): sampling examples with high structural diversity across examples from a SCFG dramatically improves comp. gen 1/2 <LINK> We double accuracy on a compositional split of Schema2QA by sampling only 5K synthetic examples from the synchronous grammar, check it out! One more #emnlp2021 paper on compositional generalization in a visually grounded setup coming up next week, stay tuned. Oh and Shana Tova!",https://arxiv.org/abs/2109.02575,"Modern semantic parsers suffer from two principal limitations. First, training requires expensive collection of utterance-program pairs. Second, semantic parsers fail to generalize at test time to new compositions/structures that have not been observed during training. Recent research has shown that automatic generation of synthetic utterance-program pairs can alleviate the first problem, but its potential for the second has thus far been under-explored. In this work, we investigate automatic generation of synthetic utterance-program pairs for improving compositional generalization in semantic parsing. Given a small training set of annotated examples and an ""infinite"" pool of synthetic examples, we select a subset of synthetic examples that are structurally-diverse and use them to improve compositional generalization. We evaluate our approach on a new split of the schema2QA dataset, and show that it leads to dramatic improvements in compositional generalization as well as moderate improvements in the traditional i.i.d setup. Moreover, structurally-diverse sampling achieves these improvements with as few as 5K examples, compared to 1M examples when sampling uniformly at random -- a 200x improvement in data efficiency. ","Finding needles in a haystack: Sampling Structurally-diverse Training