Antarlekhaka: A Comprehensive Tool for Multi-task Natural Language
  Annotation
Hrishikesh Terdalkar Arnab Bhattacharya
One of the primary obstacles in the advancement of Natural Language
Processing (NLP) technologies for low-resource languages is the lack of
annotated datasets for training and testing machine learning models. In this
paper, we present Antarlekhaka, a tool for manual annotation of a comprehensive
set of tasks relevant to NLP. The tool is Unicode-compatible,
language-agnostic, Web-deployable and supports distributed annotation by
multiple simultaneous annotators. The system sports user-friendly interfaces
for 8 categories of annotation tasks. These, in turn, enable the annotation of
a considerably larger set of NLP tasks. The task categories include two
linguistic tasks not handled by any other tool, namely, sentence boundary
detection and deciding canonical word order, which are important tasks for text
that is in the form of poetry. We propose the idea of sequential annotation
based on small text units, where an annotator performs several tasks related to
a single text unit before proceeding to the next unit. The research
applications of the proposed mode of multi-task annotation are also discussed.
Antarlekhaka outperforms other annotation tools in objective evaluation. It has
been also used for two real-life annotation tasks on two different languages,
namely, Sanskrit and Bengali. The tool is available at
https://github.com/Antarlekhaka/code.