Deep learning approaches have achieved great success in the field of Natural Language
Processing (NLP). However, directly training deep neural models often suffer from overfitting …

This paper presents the results of the premier shared task organized alongside the
Conference on Machine Translation (WMT) 2019. Participants were asked to build machine …

While syntactic dependency annotations concentrate on the surface or functional structure of
a sentence, semantic dependency annotations aim to capture between-word relationships …

Abstract Task 18 at SemEval 2015 defines Broad-Coverage Semantic Dependency Parsing
(SDP) as the problem of recovering sentence-internal predicate–argument relationships for …

In this paper we present Uniform Meaning Representation (UMR), a meaning representation
designed to annotate the semantic content of a text. UMR is primarily based on Abstract …

Many intelligent systems in business, government or academy process natural language as
an input during inference or they might even communicate with users in natural language …

We present a new release of the Czech-English parallel corpus CzEng. CzEng 1.6 consists
of about 0.5 billion words (“gigaword”) in each language. The corpus is equipped with …

For natural language processing systems, two kinds of evidence support the use of text
representations from neural language models “pretrained” on large unannotated corpora …

A language-independent representation of meaning is one of the most coveted dreams in
Natural Language Understanding. With this goal in mind, several formalisms have been …

We present PERIN, a novel permutation-invariant approach to sentence-to-graph semantic
parsing. PERIN is a versatile, cross-framework and language independent architecture for …