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

In recent years, considerable research has been dedicated to the application of neural
models in the field of natural language generation (NLG). The primary objective is to …

The ability to convey relevant and faithful information is critical for many tasks in conditional
generation and yet remains elusive for neural seq-to-seq models whose outputs often reveal …

Motivation: The scientific literature embeds an enormous amount of relational knowledge,
encompassing interactions between biomedical entities, like proteins, drugs, and symptoms …

In data-to-text (D2T) generation, training on in-domain data leads to overfitting to the data
representation and repeating training data noise. We examine how to avoid finetuning …

Research at the Interaction Lab focuses on human-agent communication using
conversational Natural Language. The ultimate goal is to create systems where humans and …

This systematic review undertakes a comprehensive analysis of current research on data-to-
text generation, identifying gaps, challenges, and future directions within the field. Relevant …

Despite significant advancements in existing models, generating text descriptions from
structured data input, known as data-to-text generation, remains a challenging task. In this …

Knowledge Graph (KG)-to-Text generation aims at generating fluent natural-language text
that accurately represents the information of a given knowledge graph. While significant …

This study introduces an enriched version of the E2E dataset, one of the most popular
language resources for data-to-text NLG. We extract intermediate representations for …