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One of the primary obstacles in the advancement of Natural LanguageProcessing (NLP) technologies for low-resource languages is the lack ofannotated datasets for training and testing machine learning models. In thispaper, we present Antarlekhaka, a tool for manual annotation of a comprehensiveset of tasks relevant to NLP. The tool is Unicode-compatible,language-agnostic, Web-deployable and supports distributed annotation bymultiple simultaneous annotators. The system sports user-friendly interfacesfor 8 categories of annotation tasks. These, in turn, enable the annotation ofa considerably larger set of NLP tasks. The task categories include twolinguistic tasks not handled by any other tool, namely, sentence boundarydetection and deciding canonical word order, which are important tasks for textthat is in the form of poetry. We propose the idea of sequential annotationbased on small text units, where an annotator performs several tasks related toa single text unit before proceeding to the next unit. The researchapplications of the proposed mode of multi-task annotation are also discussed.Antarlekhaka outperforms other annotation tools in objective evaluation. It hasbeen also used for two real-life annotation tasks on two different languages,namely, Sanskrit and Bengali. The tool is available athttps://github.com/Antarlekhaka/code.

Antarlekhaka: A Comprehensive Tool for Multi-task Natural Language Annotation