Many natural language processing (NLP) tasks involve reasoning with textual
spans, including question answering, entity recognition, and coreference
resolution. While extensive research has focused on functional architectures
for representing words and sentences, there is less work on representing
arbitrary spans of text within sentences. In this paper, we conduct a
comprehensive empirical evaluation of six span representation methods using
eight pretrained language representation models across six tasks, including two
tasks that we introduce. We find that, although some simple span
representations are fairly reliable across tasks, in general the optimal span
representation varies by task, and can also vary within different facets of
individual tasks. We also find that the choice of span representation has a
bigger impact with a fixed pretrained encoder than with a fine-tuned encoder.

本文通过对六种跨度表示方法和八个预训练语言模型在六个任务中的综合实证评估，发现即使有些简单跨度表示在多个任务中相对可靠，但一般来说最优跨度表示方法因任务而异，也可能因不同任务的不同方面而异。同时，我们还发现，与精细调整的编码器相比，跨度表示的选择对于预先训练的编码器具有更大的影响。