Papers with natural language grammars
Variance of Average Surprisal: A Better Predictor for Quality of Grammar from Unsupervised PCFG Induction (P19-1)
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| Challenge: | In unsupervised grammar induction, data likelihood is only weakly correlated with parsing accuracy, especially at convergence after multiple runs. |
| Approach: | They propose to use VAS instead of data likelihood to find better grammars by examining linguistically-motivated constraints related to syntax. |
| Outcome: | The proposed model is better suited for word order typology classification than data likelihood. |
StructFormer: Joint Unsupervised Induction of Dependency and Constituency Structure from Masked Language Modeling (2021.acl-long)
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| Challenge: | Existing models that induce grammar structures from data focus on constituency or dependency structures alone. |
| Approach: | They propose a model that can induce dependency and constituency structure at the same time. |
| Outcome: | The proposed model can induce both constituency and dependency structures at the same time. |