We lifted 4 functions out of this paper's own repositories and ran 3 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 |
|---|---|---|
| zhaoyanpeng/xcfg | extension | 3 of 4 |
| Function | Status | Where it lives |
|---|---|---|
| build_spans | Ran | zhaoyanpeng/xcfg/xcfg/data/baseline.py pointer only (licence: NONE) · get_code("5535820241c1032e") |
| get_stats | Ran | zhaoyanpeng/xcfg/xcfg/data/baseline.py pointer only (licence: NONE) · get_code("d4b3f50524ef7245") |
| random_tree | Ran | zhaoyanpeng/xcfg/xcfg/data/baseline.py pointer only (licence: NONE) · get_code("d2a5db5244d18f43") |
| remove_morph_feature | Not yet run | zhaoyanpeng/xcfg/xcfg/data/treebank.py pointer only (licence: NONE) · get_code("d733a35f871f4e54") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
In this paper we demonstrate that $\textit{context free grammar (CFG) based methods for grammar induction benefit from modeling lexical dependencies}$. This contrasts to the most popular current methods for grammar induction, which focus on discovering $\textit{either}$ constituents $\textit{or}$ dependencies. Previous approaches to marry these two disparate syntactic formalisms (e.g. lexicalized PCFGs) have been plagued by sparsity, making them unsuitable for unsupervised grammar induction. However, in this work, we present novel neural models of lexicalized PCFGs which allow us to overcome sparsity problems and effectively induce both constituents and dependencies within a single model. Experiments demonstrate that this unified framework results in stronger results on both representations than achieved when modeling either formalism alone. Code is available at https://github.com/neulab/neural-lpcfg.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2007.15135")
get_code_for_paper("2007.15135")
have("2007.15135")
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