注意音调的变化！用基屈折编码改进非标准英语的NLP
Mind Your Inflections! Improving NLP for Non-Standard Englishes with Base-Inflection Encoding
摘要：
屈折变化是世界英语的一个共同特征，如新加坡口语英语和非洲裔美国人的英语。虽然人类读者的理解通常不受非标准屈折的影响，但目前的NLP系统还不健壮。我们提出了基本屈折编码（BITE），一种标记化英语文本的方法，在重新注入语法信息作为特殊符号之前，将屈折词还原为基本形式。使用我们的编码对下游任务的预训练NLP模型进行微调，可以抵御变形对手，同时保持对干净数据的性能。使用BITE的模型在没有显式训练的情况下更好地推广到具有非标准屈折变化的方言，并且使用BITE训练时，翻译模型收敛得更快。最后，我们证明了我们的编码提高了流行的数据驱动的子词标记器的词汇效率。由于还没有以前的工作，定量评估词汇效率，我们提出了这样做的指标。
英文摘要：
Inflectional variation is a common feature of World Englishes such as Colloquial Singapore English and African American Vernacular English. Although comprehension by human readers is usually unimpaired by non-standard inflections, current NLP systems are not yet robust. We propose Base-Inflection Encoding (BITE), a method to tokenize English text by reducing inflected words to their base forms before reinjecting the grammatical information as special symbols. Fine-tuning pretrained NLP models for downstream tasks using our encoding defends against inflectional adversaries while maintaining performance on clean data. Models using BITE generalize better to dialects with non-standard inflections without explicit training and translation models converge faster when trained with BITE. Finally, we show that our encoding improves the vocabulary efficiency of popular data-driven subword tokenizers. Since there has been no prior work on quantitatively evaluating vocabulary efficiency, we propose metrics to do so.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
