We present a novel corpus consisting of orthographically variant words found
in works of 19th century U.S. literature annotated with their corresponding
"standard" word pair. We train a set of neural edit distance models to pair
these variants with their standard forms, and compare the performance of these
models to the performance of a set of neural edit distance models trained on a
corpus of orthographic errors made by L2 English learners. Finally, we analyze
the relative performance of these models in the light of different negative
training sample generation strategies, and offer concluding remarks on the
unique challenge literary orthographic variation poses to string pairing
methodologies.

我们提供了一个新颖的语料库，其中包含 19 世纪美国文学作品中的正字异构词，并且用其对应的‘标准’词对进行了注释。我们训练了一组神经编辑距离模型来将这些变体与它们的标准形式进行配对，并将这些模型的性能与训练于 L2 英语学习者拼写错误文本的神经编辑距离模型进行了比较。最后，我们分析了这些模型在不同负样本生成策略下的相对性能，并对文学正字异构变化对字符串配对方法学的独特挑战提出了总结性的观点。