Deep learning (DL) has substantially enhanced natural language processing (NLP) in
healthcare research. However, the increasing complexity of DL-based NLP necessitates …

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 …

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 …

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 …

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

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 …

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 …

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 …

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 …

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 (D2T) generation …