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COMMENTATOR: A Code-mixed Multilingual Text Annotation Framework

Rajvee Sheth, Shubh Nisar, Heenaben Prajapati, Himanshu Beniwal, Mayank Singh

TL;DR

The paper tackles the challenge of annotating code-mixed multilingual text, focusing on Hinglish, by introducing Commentator, a modular annotation framework that supports token-level language identification (LID), parts-of-speech tagging (POS), and sentence-level matrix language identification (MLI). It contrasts Commentator with five state-of-the-art tools, reporting superior operational ease and substantial annotation-time improvements (approximately 5x for LID and 2x for POS) in qualitative studies. The architecture is a modular React/Flask system with a MongoDB backend, designed for both cloud and local deployment and capable of integrating preassigned tags from external APIs or libraries. The work provides a public codebase and demonstration resources and outlines future expansions to additional tasks such as sentiment analysis, Q&A, and language generation, underscoring its practical impact for multilingual NLP annotation pipelines.

Abstract

As the NLP community increasingly addresses challenges associated with multilingualism, robust annotation tools are essential to handle multilingual datasets efficiently. In this paper, we introduce a code-mixed multilingual text annotation framework, COMMENTATOR, specifically designed for annotating code-mixed text. The tool demonstrates its effectiveness in token-level and sentence-level language annotation tasks for Hinglish text. We perform robust qualitative human-based evaluations to showcase COMMENTATOR led to 5x faster annotations than the best baseline. Our code is publicly available at \url{https://github.com/lingo-iitgn/commentator}. The demonstration video is available at \url{https://bit.ly/commentator_video}.

COMMENTATOR: A Code-mixed Multilingual Text Annotation Framework

TL;DR

The paper tackles the challenge of annotating code-mixed multilingual text, focusing on Hinglish, by introducing Commentator, a modular annotation framework that supports token-level language identification (LID), parts-of-speech tagging (POS), and sentence-level matrix language identification (MLI). It contrasts Commentator with five state-of-the-art tools, reporting superior operational ease and substantial annotation-time improvements (approximately 5x for LID and 2x for POS) in qualitative studies. The architecture is a modular React/Flask system with a MongoDB backend, designed for both cloud and local deployment and capable of integrating preassigned tags from external APIs or libraries. The work provides a public codebase and demonstration resources and outlines future expansions to additional tasks such as sentiment analysis, Q&A, and language generation, underscoring its practical impact for multilingual NLP annotation pipelines.

Abstract

As the NLP community increasingly addresses challenges associated with multilingualism, robust annotation tools are essential to handle multilingual datasets efficiently. In this paper, we introduce a code-mixed multilingual text annotation framework, COMMENTATOR, specifically designed for annotating code-mixed text. The tool demonstrates its effectiveness in token-level and sentence-level language annotation tasks for Hinglish text. We perform robust qualitative human-based evaluations to showcase COMMENTATOR led to 5x faster annotations than the best baseline. Our code is publicly available at \url{https://github.com/lingo-iitgn/commentator}. The demonstration video is available at \url{https://bit.ly/commentator_video}.
Paper Structure (21 sections, 2 equations, 5 figures, 3 tables)

This paper contains 21 sections, 2 equations, 5 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Existing Text Annotation Frameworks
  3. Web-based Annotation Tools
  4. Locally-hosted Tools
  5. COMMENTATOR
  6. The Functionalities
  7. The Annotator Panel
  8. The Admin Panel
  9. The Architecture
  10. Client Module
  11. Server Module
  12. Experiments
  13. Initial Setup and Perceived Capabilities
  14. Annotation Time
  15. Conclusion and Future Work
  16. ...and 6 more sections

Figures (5)

  • Figure 1: Commentator Framework.
  • Figure 2: The Task interface of the Commentator.
  • Figure 3: Token-Level Language Identification (LID): (a) annotation page and (b) history and edit page.
  • Figure 4: Token-Level Parts-Of-Speech Tagging (POS): (a) annotation page and (b) history and edit page.
  • Figure 5: Matrix Language Identification (MID): (a) annotation page and (b) history and edit page.