This paper provides a comprehensive analysis of the first shared task on End-to-End Natural
Language Generation (NLG) and identifies avenues for future research based on the results …

Data-to-text Generation (D2T) aims to generate textual natural language statements that can
fluently and precisely describe the structured data such as graphs, tables, and meaning …

Natural Language Generation (NLG) has made great progress in recent years due to the
development of deep learning techniques such as pre-trained language models. This …

We present a memory-augmented approach to condition an autoregressive language model
on a knowledge graph. We represent the graph as a collection of relation triples and retrieve …

Text generation from a knowledge base aims to translate knowledge triples to natural
language descriptions. Most existing methods ignore the faithfulness between a generated …

Data collection for the knowledge graph-to-text generation is expensive. As a result,
research on unsupervised models has emerged as an active field recently. However, most …

Text generation from semantic parses is to generate textual descriptions for formal
representation inputs such as logic forms and SQL queries. This is challenging due to two …

We introduce SciGen, a new challenge dataset consisting of tables from scientific articles
and their corresponding descriptions, for the task of reasoning-aware data-to-text …

We present a PaperRobot who performs as an automatic research assistant by (1)
conducting deep understanding of a large collection of human-written papers in a target …

How to generate descriptions from structured data organized in tables? Existing approaches
using neural encoder-decoder models often suffer from lacking diversity. We claim that an …