Dependency trees have been intensively used with graph neural networks for aspect-based
sentiment classification. Though being effective, such methods rely on external dependency …

Machine learning has been advancing dramatically over the past decade. Most strides are
human-based applications due to the availability of large-scale datasets; however …

Training a model for grammatical error correction (GEC) requires a set of labeled
ungrammatical/grammatical sentence pairs, but manually annotating such pairs can be …

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 investigate the unsupervised constituency parsing task, which organizes words and
phrases of a sentence into a hierarchical structure without using linguistically annotated …

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 …

Probabilistic context-free grammars (PCFGs) with neural parameterization have been shown
to be effective in unsupervised phrase-structure grammar induction. However, due to the …

Referenceless metrics (eg, CLIPScore) use pretrained vision--language models to assess
image descriptions directly without costly ground-truth reference texts. Such methods can …

For over thirty years, researchers have developed and analyzed methods for latent tree
induction as an approach for unsupervised syntactic parsing. Nonetheless, modern systems …