Segmenting vs. Chunking Rules: Unsupervised ITG Induction via Minimum Conditional Description Length

Unsupervised Chunking as Syntactic Structure Induction with a Knowledge-Transfer Approach

In this paper, we address unsupervised chunking as a new task of syntactic structure induction, which is helpful for understanding the linguistic structures of human languages as well as processing low-resource languages…

Unsupervised Transduction Grammar Induction via Minimum Description Length

Improving Neural Language Models by Segmenting, Attending, and Predicting the Future

Common language models typically predict the next word given the context. In this work, we propose a method that improves language modeling by learning to align the given context and the following phrase. The model does …

At a Glance: The Impact of Gaze Aggregation Views on Syntactic Tagging

Readers{'} eye movements used as part of the training signal have been shown to improve performance in a wide range of Natural Language Processing (NLP) tasks. Previous work uses gaze data either at the type level or at …

Unsupervised Learning of PCFGs with Normalizing Flow

Unsupervised PCFG inducers hypothesize sets of compact context-free rules as explanations for sentences. PCFG induction not only provides tools for low-resource languages, but also plays an important role in modeling lan…