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 …

In this work, we present a minimal neural model for constituency parsing based on
independent scoring of labels and spans. We show that this model is not only compatible …

We present extensions to a continuous-state dependency parsing method that makes it
applicable to morphologically rich languages. Starting with a high-performance transition …

The last few years have seen a surge in the number of accurate, fast, publicly available
dependency parsers. At the same time, the use of dependency parsing in NLP applications …

This paper describes a parsing model that combines the exact dynamic programming of
CRF parsing with the rich nonlinear featurization of neural net approaches. Our model is …

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

We reduce phrase-representation parsing to dependency parsing. Our reduction is
grounded on a new intermediate representation," head-ordered dependency trees", shown …

This paper presents a successful approach for domain adaptation of a dependency parser
via self-training. We improve parsing accuracy for out-of-domain texts with a self-training …

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 …