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

Grounded text generation systems often generate text that contains factual inconsistencies,
hindering their real-world applicability. Automatic factual consistency evaluation may help …

Most Natural Language Generation systems need to produce accurate texts. We propose a
methodology for high-quality human evaluation of the accuracy of generated texts, which is …

The surge of misinformation poses a serious problem for fact-checkers. Several initiatives for
manual fact-checking have stepped up to combat this ordeal. However, computational …

We present our Charles-UPF submission for the Shared Task on Evaluating Accuracy in
Generated Texts at INLG 2021. Our system can detect the errors automatically using a …

We investigate to which extent open large language models (LLMs) can generate coherent
and relevant text from structured data. To prevent bias from benchmarks leaked into LLM …

We hereby present our submission to the Shared Task in Evaluating Accuracy at the INLG
2021 Conference. Our evaluation protocol relies on three main components; rules and text …

The present paper summarizes an attempt we made to meet a shared task challenge on
grounding machine-generated summaries of NBA matchups (https://github. com/ehudreiter …

Throughout the last years, there has been a surge in false news spreading across the public.
Despite efforts made in alleviating" fake news", there remains a lot of ordeals when trying to …

The evaluation methodology of natural language generation (” NLG”= systems generating
text based on some kind of input) is currently in a state of a hopefully productive confusion …