Please use this identifier to cite or link to this item:Volltext verfügbar? / Dokumentlieferung
|Title:||Compositional Generalization Requires Compositional Parsers|
|Year of Publication:||2022|
|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.|
|DOI of the first publication:||10.48550/arXiv.2202.11937|
|URL of the first publication:||https://arxiv.org/abs/2202.11937|
|Link to this record:||hdl:20.500.11880/32885|
|Date of registration:||5-May-2022|
|Faculty:||P - Philosophische Fakultät|
|Department:||P - Sprachwissenschaft und Sprachtechnologie|
|Professorship:||P - Prof. Dr. Alexander Koller|
Files for this record:
There are no files associated with this item.
Items in SciDok are protected by copyright, with all rights reserved, unless otherwise indicated.