Papers › Second-Order Unsupervised Neural Dependency Parsing

Second-Order Unsupervised Neural Dependency Parsing

28 Oct 2020COLING 2020 8arXiv:2010.14720archive 2025-07-28

Songlin Yang, Yong Jiang, Wenjuan Han, Kewei Tu

Most of the unsupervised dependency parsers are based on first-order probabilistic generative models that only consider local parent-child information. Inspired by second-order supervised dependency parsing, we proposed a second-order extension of unsupervised neural dependency models that incorporate grandparent-child or sibling information. We also propose a novel design of the neural parameterization and optimization methods of the dependency models. In second-order models, the number of grammar rules grows cubically with the increase of vocabulary size, making it difficult to train lexicalized models that may contain thousands of words. To circumvent this problem while still benefiting from both second-order parsing and lexicalization, we use the agreement-based learning framework to jointly train a second-order unlexicalized model and a first-order lexicalized model. Experiments on multiple datasets show the effectiveness of our second-order models compared with recent state-of-the-art methods. Our joint model achieves a 10% improvement over the previous state-of-the-art parser on the full WSJ test set

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Dependency Grammar InductionDependency Parsing

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Dependency Grammar Induction WSJ Joint training: sibling-NDMV + L-NDMV * UAS 67.5 #1 of 1 Archive leaderboard report
Dependency Grammar Induction WSJ10 Joint training: sibling-NDMV + L-NDMV * UAS 79.9 #1 of 2 Archive leaderboard report

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