mlsanityData to 7 Oct 2026

Unsupervised Discontinuous Constituency Parsing with Mildly Context-Sensitive Grammars

arXiv 2212.09140 ↗First posted 18 Dec 2022cs.CLcited by 7

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Abstract (arXiv)

We study grammar induction with mildly context-sensitive grammars for unsupervised discontinuous parsing. Using the probabilistic linear context-free rewriting system (LCFRS) formalism, our approach fixes the rule structure in advance and focuses on parameter learning with maximum likelihood. To reduce the computational complexity of both parsing and parameter estimation, we restrict the grammar formalism to LCFRS-2 (i.e., binary LCFRS with fan-out two) and further discard rules that require O(n^6) time to parse, reducing inference to O(n^5). We find that using a large number of nonterminals is beneficial and thus make use of tensor decomposition-based rank-space dynamic programming with an embedding-based parameterization of rule probabilities to scale up the number of nonterminals. Experiments on German and Dutch show that our approach is able to induce linguistically meaningful trees with continuous and discontinuous structures

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 31 of its 85 references that are in the map. First posted on arXiv 18 Dec 2022.

Builds on

31 of 85 references.

References · 31

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19 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 3; 2024: 2; 2025: 1; 2026: 1.

7 papers.