Long documents such as academic articles and business reports have been the standard
format to detail out important issues and complicated subjects that require extra attention. An …

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

Recent language models have shown impressive multilingual performance, even when not
explicitly trained for it. Despite this, there are concerns about the quality of their outputs …

Many text generation applications require the generated text to be factually consistent with
input information. Automatic evaluation of factual consistency is challenging. Previous work …

As language models become more powerful, training and evaluation are increasingly
bottlenecked by the data and metrics used for a particular task. For example, summarization …

It is well known that the standard likelihood training and approximate decoding objectives in
neural text generation models lead to less human-like responses for open-ended tasks such …

The scarcity of comprehensive up-to-date studies on evaluation metrics for text
summarization and the lack of consensus regarding evaluation protocols continue to inhibit …

Despite recent improvements in open-domain dialogue models, state of the art models are
trained and evaluated on short conversations with little context. In contrast, the long-term …

Modern summarization models generate highly fluent but often factually unreliable outputs.
This motivated a surge of metrics attempting to measure the factuality of automatically …

Recent work pre-training Transformers with self-supervised objectives on large text corpora
has shown great success when fine-tuned on downstream NLP tasks including text …