Papers with neural dependency parsers
Bayesian Learning for Neural Dependency Parsing (N19-1)
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| Challenge: | Several approaches for dependency parsing in the small data regime have been proposed. |
| Approach: | They propose to use stochastic gradient Langevin dynamics to generate samples from the approximated posterior to overcome the computational and statistical costs of the approximate inference step. |
| Outcome: | The proposed model outperforms the biaffine model on 6 languages with less than 5k training instances and improves across five languages. |