Linguists characterize dialects by the presence, absence, and frequency of dozens of interpretable features. Detecting these features in text has applications to social science and dialectology, and can be used to assess the robustness of natural language processing systems to dialect differences. For most dialects, large-scale annotated corpora for these features are unavailable, making it difficult to train recognizers. Linguists typically define dialect features by providing a small number of minimal pairs, which are paired examples distinguished only by whether the feature is present, while holding everything else constant. In this paper, we present two multitask learning architectures for recognizing dialect features, both based on pretrained transformers. We evaluate these models on two test sets of Indian English, annotated for a total of 22 dialect features. We find these models learn to recognize many features with high accuracy; crucially, a few minimal pairs can be nearly as effective for training as thousands of labeled examples. We also demonstrate the downstream applicability of our dialect feature detection model as a dialect density measure and as a dialect classifier.

本文介绍了方言特征检测的任务，并提出了两种基于预训练转换器的多任务学习方法。评估结果表明，模型能够以很高的准确性识别许多特征，并且几个最小配对的样本可与成千上万个标注样本产生同样的效果。此外，方言特征检测具有方言密度和方言分类器的下游适用性。

学习识别方言特征