We present DART, an open domain structured DAta Record to Text generation dataset with
over 82k instances (DARTs). Data-to-Text annotations can be a costly process, especially …

Modeling human language requires the ability to not only generate fluent text but also
encode factual knowledge. However, traditional language models are only capable of …

Traditionally, most data-to-text applications have been designed using a modular pipeline
architecture, in which non-linguistic input data is converted into natural language through …

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 …

Recent developments in neural networks have led to the advance in data-to-text generation.
However, the lack of ability of neural models to control the structure of generated output can …

Generating sequential natural language descriptions from graph-structured data (eg,
knowledge graph) is challenging, partly because of the structural differences between the …

End-to-end neural data-to-text (D2T) generation has recently emerged as an alternative to
pipeline-based architectures. However, it has faced challenges in generalizing to new …

A new metric\texttt {BaryScore} to evaluate text generation based on deep contextualized
embeddings eg, BERT, Roberta, ELMo) is introduced. This metric is motivated by a new …

Assessing the quality of natural language generation (NLG) systems through human
annotation is very expensive. Additionally, human annotation campaigns are time …

The sequence-to-sequence (seq2seq) task aims at generating the target sequence based
on the given input source sequence. Traditionally, most of the seq2seq task is resolved by …