论文标题
在腹膜和代码开关环境中朝着微划分识别
Toward Micro-Dialect Identification in Diaglossic and Code-Switched Environments
论文作者
论文摘要
尽管对方言的预测是一项重要的语言处理任务，但具有广泛的应用程序，但现有的工作在很大程度上仅限于粗粒度品种。受到地理位置研究的启发，我们提出了新颖的微划分识别（MDI）的新任务，并介绍了一种具有惊人能力的新语言模型，可以预测一个单一的简短信息，以预测细粒度的品种（与城市一样小）。对于建模，我们提供了一系列新颖的空间和语言动机多任务学习模型。为了展示我们的模型的实用性，我们介绍了一个适合我们任务的新的大型阿拉伯微型（低资源）数据集。 Marbert预测，小丁香的F1为9.9％，比大多数类基线要好76倍。我们的新语言模型还为几个外部任务建立了新的最先进。
Although the prediction of dialects is an important language processing task, with a wide range of applications, existing work is largely limited to coarse-grained varieties. Inspired by geolocation research, we propose the novel task of Micro-Dialect Identification (MDI) and introduce MARBERT, a new language model with striking abilities to predict a fine-grained variety (as small as that of a city) given a single, short message. For modeling, we offer a range of novel spatially and linguistically-motivated multi-task learning models. To showcase the utility of our models, we introduce a new, large-scale dataset of Arabic micro-varieties (low-resource) suited to our tasks. MARBERT predicts micro-dialects with 9.9% F1, ~76X better than a majority class baseline. Our new language model also establishes new state-of-the-art on several external tasks.
