Span-based Semantic Parsing for Compositional Generalization

Understand

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.