We demonstrate that replacing an LSTM encoder with a self-attentive architecture can lead
to improvements to a state-of-the-art discriminative constituency parser. The use of attention …

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 …

A number of differences have emerged between modern and classic approaches to
constituency parsing in recent years, with structural components like grammars and feature …

When parsing morphologically-rich languages with neural models, it is beneficial to model
input at the character level, and it has been claimed that this is because character-level …

This paper develops a novel psycholinguistic parser and tests it against experimental and
corpus reading data. The parser builds on the recent research into memory structures, which …

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 …

Lexicalized parsing models are based on the assumptions that (i) constituents are organized
around a lexical head and (ii) bilexical statistics are crucial to solve ambiguities. In this …

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 …

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 …

We introduce a generic seq2seq parsing framework that casts constituency parsing
problems (syntactic and discourse parsing) into a series of conditional splitting decisions …