The overall translation quality reached by current machine translation (MT)
systems for high-resourced language pairs is remarkably good. Standard methods
of evaluation are not suitable nor intended to uncover the many translation
errors and quality deficiencies that still persist. Furthermore, the quality of
standard reference translations is commonly questioned and comparable quality
levels have been reached by MT alone in several language pairs. Navigating
further research in these high-resource settings is thus difficult. In this
article, we propose a methodology for creating more reliable document-level
human reference translations, called "optimal reference translations," with the
simple aim to raise the bar of what should be deemed "human translation
quality." We evaluate the obtained document-level optimal reference
translations in comparison with "standard" ones, confirming a significant
quality increase and also documenting the relationship between evaluation and
translation editing.

使用 “最佳参考翻译” 文档级可靠翻译的方法，我们提出了一种针对高资源环境中翻译质量的方法，并发现了评估与翻译编辑之间的关系。

评估最佳参考翻译

Evaluating Optimal Reference Translations

Prompt-based language models have produced encouraging results in numerous
applications, including Named Entity Recognition (NER) tasks. NER aims to
identify entities in a sentence and provide their types. However, the strong
performance of most available NER approaches is heavily dependent on the design
of discrete prompts and a verbalizer to map the model-predicted outputs to
entity categories, which are complicated undertakings. To address these
challenges, we present ContrastNER, a prompt-based NER framework that employs
both discrete and continuous tokens in prompts and uses a contrastive learning
approach to learn the continuous prompts and forecast entity types. The
experimental results demonstrate that ContrastNER obtains competitive
performance to the state-of-the-art NER methods in high-resource settings and
outperforms the state-of-the-art models in low-resource circumstances without
requiring extensive manual prompt engineering and verbalizer design.

该论文提出了 ContrastNER，一个基于提示的命名实体识别框架，使用离散和连续的标记来学习对话。通过对比学习的方法，ContrastNER 在高资源设置中获得了与现有技术同样的性能，并在低资源环境中胜过现有模型。