Span-based Semantic Parsing for Compositional Generalization
This addresses the challenge of compositional generalization for semantic parsing systems, which is incremental as it builds on prior span-based methods.
The paper tackles the problem of compositional generalization in semantic parsing, where sequence-to-sequence models fail to generalize to new structures built from known components, and demonstrates that a span-based parser improves average accuracy from 61.0 to 88.9 on splits requiring such generalization.
Despite the success of sequence-to-sequence (seq2seq) models in semantic parsing, recent work has shown that they fail in compositional generalization, i.e., the ability to generalize to new structures built of components observed during training. In this work, we posit that a span-based parser should lead to better compositional generalization. we propose SpanBasedSP, a parser that predicts a span tree over an input utterance, explicitly encoding how partial programs compose over spans in the input. SpanBasedSP extends Pasupat et al. (2019) to be comparable to seq2seq models by (i) training from programs, without access to gold trees, treating trees as latent variables, (ii) parsing a class of non-projective trees through an extension to standard CKY. On GeoQuery, SCAN and CLOSURE datasets, SpanBasedSP performs similarly to strong seq2seq baselines on random splits, but dramatically improves performance compared to baselines on splits that require compositional generalization: from $61.0 \rightarrow 88.9$ average accuracy.