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

Currently, there is little agreement as to how Natural Language Generation (NLG) systems
should be evaluated, with a particularly high degree of variation in the way that human …

Human evaluation is indispensable and inevitable for assessing the quality of texts
generated by machine learning models or written by humans. However, human evaluation is …

The paper surveys evaluation methods of natural language generation (NLG) systems that
have been developed in the last few years. We group NLG evaluation methods into three …

In order to simplify sentences, several rewriting operations can be performed, such as
replacing complex words per simpler synonyms, deleting unnecessary information, and …

Data quality is crucial for training accurate, unbiased, and trustworthy machine learning
models as well as for their correct evaluation. Recent work, however, has shown that even …

A significant share of political discourse occurs online on social media platforms.
Policymakers and researchers try to understand the role of social media design in shaping …

The lack of time-efficient and reliable evaluation methods hamper the development of
conversational dialogue systems (chatbots). Evaluations requiring humans to converse with …

Integrating user feedback is one of the pillars for building successful products. However, this
feedback is generally collected in an unstructured free-text form, which is challenging to …

In many linguistic fields requiring annotated data, multiple interpretations of a single item are
possible. Multi-label annotations more accurately reflect this possibility. However, allowing …