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。

使用神经编辑距离模型将正字文言文词汇配对为标准等价词

Pairing Orthographically Variant Literary Words to Standard Equivalents  Using Neural Edit Distance Models

Natural language correction has the potential to help language learners
improve their writing skills. While approaches with separate classifiers for
different error types have high precision, they do not flexibly handle errors
such as redundancy or non-idiomatic phrasing. On the other hand, word and
phrase-based machine translation methods are not designed to cope with
orthographic errors, and have recently been outpaced by neural models.
Motivated by these issues, we present a neural network-based approach to
language correction. The core component of our method is an encoder-decoder
recurrent neural network with an attention mechanism. By operating at the
character level, the network avoids the problem of out-of-vocabulary words. We
illustrate the flexibility of our approach on dataset of noisy, user-generated
text collected from an English learner forum. When combined with a language
model, our method achieves a state-of-the-art $F_{0.5}$-score on the CoNLL 2014
Shared Task. We further demonstrate that training the network on additional
data with synthesized errors can improve performance.

本文介绍了一种基于神经网络的自然语言校正方法，该方法以字符级别操作避免了词汇表外的问题，并展示了在语言学习者论坛收集的数据集上的灵活性和性能。与语言建模相结合后，该方法在 CoNLL 2014 共享任务中实现了最先进的 $F_{0.5}$- 分数。