Compositional Generalization Requires Compositional Parsers
A growing body of research has focused on the task of \textit{compositional generalization}, the ability of a semantic parser to dynamically combine known linguistic elements in novel structures. We analyze the accuracy of different parsers on the recent COGS corpus (Kim and Linzen, 2020). While lexical generalization tasks are solvable by almost all existing models, tasks involving changes to the linguistic structure are hard for even the best sequence-to-sequence models. Structural generalization tasks can be solved with models that have compositionality built in; we present new results confirming this from the AM parser (Groschwitz et al., 2021). We further analyze the role of syntactic generalization in compositional generalization, and we discuss ramifications for the design of both semantic parsers and compositional generalization datasets.
Code (0)
등록된 구현이 없습니다.
Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Compositional Generalization Requires Compositional Parsers
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 systemati…
Compositional generalization with a broad-coverage semantic parser
We show how the AM parser, a compositional semantic parser (Groschwitz et al., 2018) can solve compositional generalization on the COGS dataset. It is the first semantic parser that achieves high accuracy on both natural…
Finding needles in a haystack: Sampling Structurally-diverse Training Sets from Synthetic Data for Compositional Generalization
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/struct…
Semantic ParsingCompositional Generalization in Multilingual Semantic Parsing over Wikidata
Semantic parsing (SP) allows humans to leverage vast knowledge resources through natural interaction. However, parsers are mostly designed for and evaluated on English resources, such as CFQ (Keysers et al., 2020), the c…
Cross-Lingual TransferSemantic ParsingZero-Shot Cross-Lingual TransferDetecting Compositionally Out-of-Distribution Examples in Semantic Parsing
While neural networks are ubiquitous in state-of-the-art semantic parsers, it has been shown that most standard models suffer from dramatic performance losses when faced with compositionally out-of-distribution (OOD) dat…
Out of Distribution (OOD) DetectionSemantic Parsing