LAGr: Labeling Aligned Graphs for Improving Systematic Generalization in Semantic Parsing
This addresses the challenge of compositional generalization in semantic parsing for natural language processing, offering a novel approach to improve performance on complex tasks.
The paper tackles the problem of systematic generalization in semantic parsing by proposing LAGr, an algorithm that produces meaning representations as graphs rather than sequences, achieving significant improvements on COGS and CFQ benchmarks.
Semantic parsing is the task of producing a structured meaning representation for natural language utterances or questions. Recent research has pointed out that the commonly-used sequence-to-sequence (seq2seq) semantic parsers struggle to generalize systematically, i.e. to handle examples that require recombining known knowledge in novel settings. In this work, we show that better systematic generalization can be achieved by producing the meaning representation (MR) directly as a graph and not as a sequence. To this end we propose LAGr, the Labeling Aligned Graphs algorithm that produces semantic parses by predicting node and edge labels for a complete multi-layer input-aligned graph. The strongly-supervised LAGr algorithm requires aligned graphs as inputs, whereas weakly-supervised LAGr infers alignments for originally unaligned target graphs using an approximate MAP inference procedure. On the COGS and CFQ compositional generalization benchmarks the strongly- and weakly- supervised LAGr algorithms achieve significant improvements upon the baseline seq2seq parsers.