We lifted 1 functions out of this paper's own repositories and ran 1 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| facebookresearch/colorlessgreenRNNs | canonical | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| query_KenLM | Ran | facebookresearch/colorlessgreenRNNs/src/syntactic_testsets/evaluate_utils.py pointer only (licence: NOASSERTION) · get_code("f10c159718f91bd8") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Recurrent neural networks (RNNs) have achieved impressive results in a variety of linguistic processing tasks, suggesting that they can induce non-trivial properties of language. We investigate here to what extent RNNs learn to track abstract hierarchical syntactic structure. We test whether RNNs trained with a generic language modeling objective in four languages (Italian, English, Hebrew, Russian) can predict long-distance number agreement in various constructions. We include in our evaluation nonsensical sentences where RNNs cannot rely on semantic or lexical cues ("The colorless green ideas I ate with the chair sleep furiously"), and, for Italian, we compare model performance to human intuitions. Our language-model-trained RNNs make reliable predictions about long-distance agreement, and do not lag much behind human performance. We thus bring support to the hypothesis that RNNs are not just shallow-pattern extractors, but they also acquire deeper grammatical competence.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1803.11138")
get_code_for_paper("1803.11138")
have("1803.11138")
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