Dialogue systems are a popular natural language processing (NLP) task as it is promising in
real-life applications. It is also a complicated task since many NLP tasks deserving study are …

Abstract Evaluation practices in natural language generation (NLG) have many known flaws,
but improved evaluation approaches are rarely widely adopted. This issue has become …

The quality of texts generated by natural language generation (NLG) systems is hard to
measure automatically. Conventional reference-based metrics, such as BLEU and ROUGE …

Text evaluation has historically posed significant challenges, often demanding substantial
labor and time cost. With the emergence of large language models (LLMs), researchers …

Multi-dimensional evaluation is the dominant paradigm for human evaluation in Natural
Language Generation (NLG), ie, evaluating the generated text from multiple explainable …

Even though machine learning has become the major scene in dialogue research
community, the real breakthrough has been blocked by the scale of data available. To …

In the last few years, a large number of automatic evaluation metrics have been proposed for
evaluating Natural Language Generation (NLG) systems. The rapid development and …

Dialogue systems have attracted more and more attention. Recent advances on dialogue
systems are overwhelmingly contributed by deep learning techniques, which have been …

This paper delves into the pivotal role of prompt engineering in unleashing the capabilities
of Large Language Models (LLMs). Prompt engineering is the process of structuring input …

This paper surveys the current state of the art in Natural Language Generation (NLG),
defined as the task of generating text or speech from non-linguistic input. A survey of NLG is …