The growth of social media has encouraged the written use of African American Vernacular English (AAVE), which has traditionally been used only in oral contexts. However, NLP models have historically been developed using dominant English varieties, such as Standard American English (SAE), due to text corpora availability. We investigate the performance of GPT-2 on AAVE text by creating a dataset of intent-equivalent parallel AAVE/SAE tweet pairs, thereby isolating syntactic structure and AAVE- or SAE-specific language for each pair. We evaluate each sample and its GPT-2 generated text with pretrained sentiment classifiers and find that while AAVE text results in more classifications of negative sentiment than SAE, the use of GPT-2 generally increases occurrences of positive sentiment for both. Additionally, we conduct human evaluation of AAVE and SAE text generated with GPT-2 to compare contextual rigor and overall quality.

本研究使用AAVE/SAE推文对AAVE文本的语法结构、AAVE特定语言和SAE特定语形进行隔离，以探究GPT-2在AAVE文本上的表现，并使用预训练的情感分类器评估每个样本及其GPT-2生成的文本，结果显示AAVE文本的负面情感分类比SAE文本更多，GPT-2的使用一般会增加正面情感的出现。此外，本研究进行了人类评估，用以比较GPT-2生成的AAVE文本与SAE文本在上下文的严谨性和总体质量。

探究基于 Transformer 的文本生成中的非裔美国人土语英语