Papers › Shared Logistic Normal Distributions for Soft Parameter Tying in Unsupervised Grammar Induction
Shared Logistic Normal Distributions for Soft Parameter Tying in Unsupervised Grammar Induction
Shay Cohen, Noah A. Smith
We present a family of priors over probabilistic grammar weights, called the shared logistic normal distribution. This family extends the partitioned logistic normal distribution, enabling factored covariance between the probabilities of different derivation events in the probabilistic grammar, providing a new way to encode prior knowledge about an unknown grammar. We describe a variational EM algorithm for learning a probabilistic grammar based on this family of priors. We then experiment with unsupervised dependency grammar induction and show significant improvements using our model for both monolingual learning and bilingual learning with a non-parallel, multilingual corpus.
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Unsupervised Dependency Parsing | Penn Treebank | Shared Logistic Normal DMV | UAS | 41.4 | #5 of 6 | Archive leaderboard | report |
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