Abstract The 2020 Shared Task at the Conference for Computational Language Learning
(CoNLL) was devoted to Meaning Representation Parsing (MRP) across frameworks and …

Semantic representation is a way of expressing the meaning of a text that can be processed
by a machine to serve a particular natural language processing (NLP) task that usually …

Abstract The 2019 Shared Task at the Conference for Computational Language Learning
(CoNLL) was devoted to Meaning Representation Parsing (MRP) across frameworks. Five …

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

This paper presents new state-of-the-art models for three tasks, part-of-speech tagging,
syntactic parsing, and semantic parsing, using the cutting-edge contextualized embedding …

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 …

Graph-based semantic parsing aims to represent textual meaning through directed graphs.
As one of the most promising general-purpose meaning representations, these structures …

Transition-based parsers implemented with Pointer Networks have become the new state of
the art in dependency parsing, excelling in producing labelled syntactic trees and …

Business process management encompasses a variety of tasks that can be solved system-
aided but usually require formal process representations, ie process models. However, it …

Abstract Domain adaptation methods often exploit domain-transferable input features, aka
pivots. The task of Aspect and Opinion Term Extraction presents a special challenge for …