Recent advances in automatic evaluation metrics for text have shown that deep contextualized word representations, such as those generated by BERT encoders, are helpful for designing metrics that correlate well with human judgements. At the same time, it has been argued that contextualized word representations exhibit sub-optimal statistical properties for encoding the true similarity between words or sentences. In this paper, we present two techniques for improving encoding representations for similarity metrics: a batch-mean centering strategy that improves statistical properties; and a computationally efficient tempered Word Mover Distance, for better fusion of the information in the contextualized word representations. We conduct numerical experiments that demonstrate the robustness of our techniques, reporting results over various BERT-backbone learned metrics and achieving state of the art correlation with human ratings on several benchmarks.

本文提出了两种改进相似度度量的编码表示方法，包括基于批次平均居中策略改进统计意义和在BERT编码器中使用计算效率更高的温和Word Mover距离来更好地融合上下文词表示信息，实验表明，这些方法的鲁棒性得到了证明，并在各种BERT骨干学习指标上取得了与人类等级的业界最先进的相关性。

使用批量居中和缓和词移距离来提高文本生成评估