Large language models (LLMs) can learn to perform a wide range of natural language tasks
from just a handful of in-context examples. However, for generating strings from highly …

Latent variable models (LVMs) with discrete compositional latents are an important but
challenging setting due to a combinatorially large number of possible configurations of the …

Sequence-to-sequence learning with neural networks has become the de facto standard for
sequence modeling. This approach typically models the local distribution over the next …

Recent advancements in pre-trained language models (PLMs) have demonstrated that
these models possess some degree of syntactic awareness. To leverage this knowledge, we …

We describe a neural transducer that maintains the flexibility of standard sequence-to-
sequence (seq2seq) models while incorporating hierarchical phrases as a source of …

We address unsupervised discontinuous constituency parsing, where we observe a high
variance in the performance of the only previous model in the literature. We propose to build …

Neural lexicalized PCFGs (L-PCFGs) have been shown effective in grammar induction.
However, to reduce computational complexity, they make a strong independence …

Video-aided grammar induction aims to leverage video information for finding more accurate
syntactic grammars for accompanying text. While previous work focuses on building systems …

We study grammar induction with mildly context-sensitive grammars for unsupervised
discontinuous parsing. Using the probabilistic linear context-free rewriting system (LCFRS) …

Hidden Markov Models (HMMs) and Probabilistic Context-Free Grammars (PCFGs) are
widely used structured models, both of which can be represented as factor graph grammars …