Although neural-based machine translation evaluation metrics, such as COMET
or BLEURT, have achieved strong correlations with human judgements, they are
sometimes unreliable in detecting certain phenomena that can be considered as
critical errors, such as deviations in entities and numbers. In contrast,
traditional evaluation metrics, such as BLEU or chrF, which measure lexical or
character overlap between translation hypotheses and human references, have
lower correlations with human judgements but are sensitive to such deviations.
In this paper, we investigate several ways of combining the two approaches in
order to increase robustness of state-of-the-art evaluation methods to
translations with critical errors. We show that by using additional information
during training, such as sentence-level features and word-level tags, the
trained metrics improve their capability to penalize translations with specific
troublesome phenomena, which leads to gains in correlation with human judgments
and on recent challenge sets on several language pairs.

本研究探讨将传统评估方法如 BLEU 或 chrF 与基于神经网络的机器翻译评估方法如 COMET 或 BLEURT 相结合，通过使用附加的诸如句子级特征和单词级标签等额外信息训练评估指标，提高了最新状态下的机器翻译评估方法的稳健性，从而在几种语言对上提高了与人类判断的相关性和在挑战数据集上获得了更好的表现。

BLEU Meets COMET: 结合词汇和神经度量以实现机器翻译评估的稳健性

BLEU Meets COMET: Combining Lexical and Neural Metrics Towards Robust  Machine Translation Evaluation

This paper presents the results of research on supervised extractive text
summarisation for scientific articles. We show that a simple sequential tagging
model based only on the text within a document achieves high results against a
simple classification model. Improvements can be achieved through additional
sentence-level features, though these were minimal. Through further analysis,
we show the potential of the sequential model relying on the structure of the
document depending on the academic discipline which the document is from.

通过研究，本文在科学文章的有监督摘要提取方面展现了基于仅使用文档内文本的简单序列标记模型对简单分类模型的高效结果，并对句子级特征进行了最小化改进。通过进一步分析，我们展示了序列模型依赖文档结构且取决于学科领域的潜力。