Unsupervised Linguistically-Driven Reliable Dependency Parses Detection and Self-Training for Adaptation to the Biomedical Domain

Passing Parser Uncertainty to the Transformer. Labeled Dependency Distributions for Neural Machine Translation.

Existing syntax-enriched neural machine translation (NMT) models work either with the single most-likely unlabeled parse or the set of n-best unlabeled parses coming out of an external parser. Passing a single or n-best …

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…

Reranking Machine Translation Hypotheses with Structured and Web-based Language Models

In this paper, we investigate the use of linguistically motivated and computationally efficient structured language models for reranking N-best hypotheses in a statistical machine translation system. These language model…

A Method to Generate Simplified Systemic Functional Parses from Dependency Parses

Finding Hierarchical Structure in Neural Stacks Using Unsupervised Parsing

Neural network architectures have been augmented with differentiable stacks in order to introduce a bias toward learning hierarchy-sensitive regularities. It has, however, proven difficult to assess the degree to which s…