Generalization without Systematicity¶
Lake, B. M., & Baroni, M. (2018). Generalization without Systematicity: On the Compositional Skills of Sequence-to-Sequence Recurrent Networks. ICML 2018.
Cited by¶
1 citation across 1 artifact.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Compositionality
- Connectionist models face the challenge of achieving compositional generalization: Lake-Baroni (2018) demonstrate with the SCAN benchmark that transformer language models, despite their empirical success, fail at systematic compositional generalization tasks.
This sourceIntroduces the SCAN benchmark; seq2seq RNNs succeed by mix-and-match but fail systematic compositional generalization (supports D22-230). NOTE: the paper tests RNN seq2seq models — the .md body at line 92 attributes the SCAN failure to 'transformer language models,' which is a factual error (see flag).
- Connectionist models face the challenge of achieving compositional generalization: Lake-Baroni (2018) demonstrate with the SCAN benchmark that transformer language models, despite their empirical success, fail at systematic compositional generalization tasks.
Verification¶
Does it exist? Not checked yet. This entry carries no identifier to resolve. It was extracted from the citation as written in the article, normalized, and deduplicated against the rest of the registry.
Does it back the claim? Not recorded. The single citation of this work carries no recorded support check.
Support is checked per citation rather than per work — the same source can be cited soundly in one article and wrongly in another. Per-citation recording began recently, so a citation with no recorded check is a gap in the record rather than evidence it went unchecked.
See how references were verified.
Registry ID ref:3413ff799872 · see in the full table