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 …

Transcribing structured data into natural language descriptions has emerged as a
challenging task, referred to as “data-to-text”. These structures generally regroup multiple …

Although Seq2Seq models for table-to-text generation have achieved remarkable progress,
modeling table representation in one dimension is inadequate. This is because (1) the table …

In recent years, deep neural network has achieved great success in solving many natural
language processing tasks. Particularly, substantial progress has been made on neural text …

Data-to-text generation models face challenges in ensuring data fidelity by referring to the
correct input source. To inspire studies in this area, Wiseman et al.(2017) introduced the …

We propose a data-to-text generation model with two modules, one for tracking and the
other for text generation. Our tracking module selects and keeps track of salient information …

With the rapid development of deep learning, training Big Models (BMs) for multiple
downstream tasks becomes a popular paradigm. Researchers have achieved various …

Table-to-text generation is a subtask of datato-text generation which aims to generate
naltural language text based on input table. Pretraining techniques have achieved great …

The comprehensive descriptions for factual attribute-value tables, which should be accurate,
informative and loyal, can be very helpful for end users to understand the structured data in …

Quantitative information plays an important part in the financial and data analysis areas.
Prior work relied on pattern-matching methods and complex hand-crafted rules to extract …