We show that constituency parsing benefits from unsupervised pre-training across a variety
of languages and a range of pre-training conditions. We first compare the benefits of no pre …

Head-driven phrase structure grammar (HPSG) enjoys a uniform formalism representing rich
contextual syntactic and even semantic meanings. This paper makes the first attempt to …

Syntactic and semantic parsing has been investigated for decades, which is one primary
topic in the natural language processing community. This article aims for a brief survey on …

Attention mechanisms have improved the performance of NLP tasks while allowing models
to remain explainable. Self-attention is currently widely used, however interpretability is …

Estimating probability distribution is one of the core issues in the NLP field. However, in both
deep learning (DL) and pre-DL eras, unlike the vast applications of linear-chain CRF in …

Constituency parsing and nested named entity recognition (NER) are similar tasks since
they both aim to predict a collection of nested and non-crossing spans. In this work, we cast …

Constituency parsing is a fundamental and important task for natural language
understanding, where a good representation of contextual information can help this task. N …

We propose a method for unsupervised parsing based on the linguistic notion of a
constituency test. One type of constituency test involves modifying the sentence via some …

Dependency and constituent trees are widely used by many artificial intelligence
applications for representing the syntactic structure of human languages. Typically, these …

We present a simple and unified approach for both continuous and discontinuous
constituency parsing via autoregressive span selection. Constituency parsing aims to …