Unsupervised Dependency Parsing with Transferring Distribution via Parallel Guidance and Entropy Regularization

Cross-lingual Transfer for Unsupervised Dependency Parsing Without Parallel Data

Cross-Lingual Syntactic Transfer through Unsupervised Adaptation of Invertible Projections

Cross-lingual transfer is an effective way to build syntactic analysis tools in low-resource languages. However, transfer is difficult when transferring to typologically distant languages, especially when neither annotat…

Corpus-Based Induction of Syntactic Structure: Models of Dependency and Constituency

We present a generative model for the unsupervised learning of dependency structures. We also describe the multiplicative combination of this dependency model with a model of linear constituency. The product model outper…

Adapting Unsupervised Syntactic Parsing Methodology for Discourse Dependency Parsing

One of the main bottlenecks in developing discourse dependency parsers is the lack of annotated training data. A potential solution is to utilize abundant unlabeled data by using unsupervised techniques, but there is so …

Lock-Free Parallel Perceptron for Graph-based Dependency Parsing

Dependency parsing is an important NLP task. A popular approach for dependency parsing is structured perceptron. Still, graph-based dependency parsing has the time complexity of $O(n^3)$, and it suffers from slow trainin…