There are several issues with the existing general machine translation or
natural language generation evaluation metrics, and question-answering (QA)
systems are indifferent in that context. To build robust QA systems, we need
the ability to have equivalently robust evaluation systems to verify whether
model predictions to questions are similar to ground-truth annotations. The
ability to compare similarity based on semantics as opposed to pure string
overlap is important to compare models fairly and to indicate more realistic
acceptance criteria in real-life applications. We build upon the first to our
knowledge paper that uses transformer-based model metrics to assess semantic
answer similarity and achieve higher correlations to human judgement in the
case of no lexical overlap. We propose cross-encoder augmented bi-encoder and
BERTScore models for semantic answer similarity, trained on a new dataset
consisting of name pairs of US-American public figures. As far as we are
concerned, we provide the first dataset of co-referent name string pairs along
with their similarities, which can be used for training.

提出了利用基于 Transformer 模型度量方法对自然语言生成及问答系统进行语义相似度评估的框架，并使用 US-American public figures 的同指名字对数据集进行训练，取得较高的语义相似度度量结果。