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 …

Parsing accuracy using efficient greedy transition systems has improved dramatically in
recent years thanks to neural networks. Despite striking results in dependency parsing …

In this work, we propose a novel constituency parsing scheme. The model predicts a vector
of real-valued scalars, named syntactic distances, for each split position in the input …

Discourse parsing is an integral part of understanding information flow and argumentative
structure in documents. Most previous research has focused on inducing and evaluating …

This study focuses on an essential precondition for reproducibility in computational
linguistics: the willingness of authors to share relevant source code and data. Ten years after …

Dynamic oracles provide strong supervision for training constituency parsers with
exploration, but must be custom defined for a given parser's transition system. We explore …

We use parsing as sequence labeling as a common framework to learn across constituency
and dependency syntactic abstractions. To do so, we cast the problem as multitask learning …

Sequence tagging models for constituent parsing are faster, but less accurate than other
types of parsers. In this work, we address the following weaknesses of such constituent …

Non-local features have been exploited by syntactic parsers for capturing dependencies
between sub output structures. Such features have been a key to the success of state-of-the …

We introduce a novel transition system for discontinuous constituency parsing. Instead of
storing subtrees in a stack--ie a data structure with linear-time sequential access--the …