This paper surveys the current state of the art in Natural Language Generation (NLG),
defined as the task of generating text or speech from non-linguistic input. A survey of NLG is …

Neural encoder-decoder models for language generation can be trained to predict words
directly from linguistic or non-linguistic inputs. When generating with these so-called end-to …

Abstract Language generation from purely semantic representations is a challenging task.
This paper addresses generating English from the Abstract Meaning Representation (AMR) …

Abstract The Surface Realisation (SR) Task was a new task at Generation Challenges 2011,
and had two tracks:(1) Shallow: map** from shallow input representations to realisations; …

The task of AMR-to-text generation is to generate grammatical text that sustains the semantic
meaning for a given AMR graph. We at-tack the task by first partitioning the AMR graph into …

(57) ABSTRACT A method includes receiving one or more natural language dependency
parse trees as input. A hardware processor is used for processing the dependency parse …

This article attempts to place dependency annotation options on a solid theoretical and
applied footing. By verifying the validity of some basic choices of the current dependency …

Healthcare organizations are in a continuous effort to improve health outcomes, reduce
costs, and enhance patient experience of care. Data is essential to measure and help …

We present a novel syntax-based natural language generation system that is trainable from
unaligned pairs of input meaning representations and output sentences. It is divided into …

We present the first dataset targeted at end-to-end NLG in Czech in the restaurant domain,
along with several strong baseline models using the sequence-to-sequence approach …