Natural Language Generation Datasets

Natural language generation (NLG) datasets are crucial for training and evaluating models that produce human-quality text. Current research focuses on improving dataset quality by identifying and mitigating noise, enhancing evaluation metrics' robustness against adversarial examples, and developing more linguistically diverse and representative corpora, particularly for handling named entities. These efforts aim to create more reliable and accurate NLG systems, addressing challenges like factual consistency and uncertainty quantification, ultimately leading to advancements in various applications such as dialogue systems, summarization, and machine translation. The development of improved datasets and evaluation methods is essential for the continued progress of the field.