Survey of the state of the art in natural language generation: Core tasks, applications and evaluation
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 …
defined as the task of generating text or speech from non-linguistic input. A survey of NLG is …
Deep graph convolutional encoders for structured data to text generation
D Marcheggiani, L Perez-Beltrachini - arXiv preprint arXiv:1810.09995, 2018 - arxiv.org
Most previous work on neural text generation from graph-structured data relies on standard
sequence-to-sequence methods. These approaches linearise the input graph to be fed to a …
sequence-to-sequence methods. These approaches linearise the input graph to be fed to a …
Enhancing AMR-to-text generation with dual graph representations
LFR Ribeiro, C Gardent, I Gurevych - arXiv preprint arXiv:1909.00352, 2019 - arxiv.org
Generating text from graph-based data, such as Abstract Meaning Representation (AMR), is
a challenging task due to the inherent difficulty in how to properly encode the structure of a …
a challenging task due to the inherent difficulty in how to properly encode the structure of a …
Manifesting construction activity scenes via image captioning
This study proposed an automated method for manifesting construction activity scenes by
image captioning–an approach rooted in computer vision and natural language generation …
image captioning–an approach rooted in computer vision and natural language generation …
End-to-end content and plan selection for data-to-text generation
Learning to generate fluent natural language from structured data with neural networks has
become an common approach for NLG. This problem can be challenging when the form of …
become an common approach for NLG. This problem can be challenging when the form of …
Natural language interfaces to data
Recent advances in natural language understanding and processing have resulted in
renewed interest in natural language interfaces to data, which provide an easy mechanism …
renewed interest in natural language interfaces to data, which provide an easy mechanism …
Line graph enhanced AMR-to-text generation with mix-order graph attention networks
Efficient structure encoding for graphs with labeled edges is an important yet challenging
point in many graph-based models. This work focuses on AMR-to-text generation–A graph …
point in many graph-based models. This work focuses on AMR-to-text generation–A graph …
Document automation architectures and technologies: A survey
This paper surveys the current state of the art in document automation (DA). The objective of
DA is to reduce the manual effort during the generation of documents by automatically …
DA is to reduce the manual effort during the generation of documents by automatically …
NeuralREG: An end-to-end approach to referring expression generation
Traditionally, Referring Expression Generation (REG) models first decide on the form and
then on the content of references to discourse entities in text, typically relying on features …
then on the content of references to discourse entities in text, typically relying on features …
Neural Methods for Data-to-text Generation
The neural boom that has sparked natural language processing (NLP) research throughout
the last decade has similarly led to significant innovations in data-to-text generation (D2T) …
the last decade has similarly led to significant innovations in data-to-text generation (D2T) …