LSCP: Enhanced Large Scale Colloquial Persian Language Understanding
This addresses the problem of low-resource language processing for researchers and developers working with Persian, though it is incremental as it primarily provides a new dataset rather than a novel method.
The authors tackled the lack of resources for colloquial Persian language understanding by creating LSCP, a large-scale dataset with 120M sentences from 27M tweets annotated with parsing trees, part-of-speech tags, sentiment polarity, and translations in five languages.
Language recognition has been significantly advanced in recent years by means of modern machine learning methods such as deep learning and benchmarks with rich annotations. However, research is still limited in low-resource formal languages. This consists of a significant gap in describing the colloquial language especially for low-resourced ones such as Persian. In order to target this gap for low resource languages, we propose a "Large Scale Colloquial Persian Dataset" (LSCP). LSCP is hierarchically organized in a semantic taxonomy that focuses on multi-task informal Persian language understanding as a comprehensive problem. This encompasses the recognition of multiple semantic aspects in the human-level sentences, which naturally captures from the real-world sentences. We believe that further investigations and processing, as well as the application of novel algorithms and methods, can strengthen enriching computerized understanding and processing of low resource languages. The proposed corpus consists of 120M sentences resulted from 27M tweets annotated with parsing tree, part-of-speech tags, sentiment polarity and translation in five different languages.