Natural Language Generation (NLG) has improved exponentially in recent years thanks to
the development of sequence-to-sequence deep learning technologies such as Transformer …

Text Generation aims to produce plausible and readable text in human language from input
data. The resurgence of deep learning has greatly advanced this field, in particular, with the …

Charts are commonly used for exploring data and communicating insights. Generating
natural language summaries from charts can be very helpful for people in inferring key …

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 …

In the last few years, the natural language processing community has witnessed advances
in neural representations of free texts with transformer-based language models (LMs). Given …

Since a vast number of tables can be easily collected from web pages, spreadsheets, PDFs,
and various other document types, a flurry of table pre-training frameworks have been …

Tables are often created with hierarchies, but existing works on table reasoning mainly focus
on flat tables and neglect hierarchical tables. Hierarchical tables challenge existing methods …

Charts are very popular to analyze data and convey important insights. People often analyze
visualizations to answer open-ended questions that require explanatory answers …

Large Language Models (LLMs) have demonstrated remarkable performance across a wide
range of natural language processing tasks. However, their remarkable parameter size and …

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 …