Compositional Generalization Requires Compositional Parsers

• URL: http://arxiv.org/abs/2202.11937v1
• Date: Thu, 24 Feb 2022 07:36:35 GMT
• Title: Compositional Generalization Requires Compositional Parsers
• Authors: Pia Wei{\ss}enhorn, Yuekun Yao, Lucia Donatelli, Alexander Koller
• Abstract summary: We compare sequence-to-sequence models and models guided by compositional principles on the recent COGS corpus. We show structural generalization is a key measure of compositional generalization and requires models that are aware of complex structure.
• Score: 69.77216620997305
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A rapidly growing body of research on compositional generalization investigates the ability of a semantic parser to dynamically recombine linguistic elements seen in training into unseen sequences. We present a systematic comparison of sequence-to-sequence models and models guided by compositional principles on the recent COGS corpus (Kim and Linzen, 2020). Though seq2seq models can perform well on lexical tasks, they perform with near-zero accuracy on structural generalization tasks that require novel syntactic structures; this holds true even when they are trained to predict syntax instead of semantics. In contrast, compositional models achieve near-perfect accuracy on structural generalization; we present new results confirming this from the AM parser (Groschwitz et al., 2021). Our findings show structural generalization is a key measure of compositional generalization and requires models that are aware of complex structure.

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