QQ: A Toolkit for Language Identifiers and Metadata

2026-02-28

AI Summary

This work addresses the persistent challenge in multilingual NLP research caused by inconsistent language identifier standards—such as BCP-47, ISO 639-1, and Glottocode—which complicate data mapping and scalability. To resolve this, the authors introduce QwanQwa (QQ), a lightweight Python toolkit that, for the first time, integrates heterogeneous linguistic metadata into a traversable graph structure. This unified representation harmonizes disparate language identifiers and enables efficient querying and exploratory analysis across multiple dimensions, including language family, geographic region, and writing system. By offering a standardized, intuitive, and scalable solution for managing metadata across thousands of languages, QwanQwa significantly enhances the extensibility and usability of language identification in multilingual NLP tasks.

Technology Category

• Natural Language Processing: Machine Translation, Multilinguality, Cross-Lingual NLP
• Knowledge Representation and Reasoning: Knowledge Representation Languages
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies

Abstract

The growing number of languages considered in multilingual NLP, including new datasets and tasks, poses challenges regarding properly and accurately reporting which languages are used and how. For example, datasets often use different language identifiers; some use BCP-47 (e.g. en_Latn), others use ISO 639-1 (en), and more linguistically oriented datasets use Glottocodes (stan1293). Mapping between identifiers is manageable for a few dozen languages, but becomes unscalable when dealing with thousands. We introduce QwanQwa, a light-weight Python toolkit for unified language metadata management. QQ integrates multiple language resources into a single interface, provides convenient normalization and mapping between language identifiers, and affords a graph-based structure that enables traversal across families, regions, writing systems, and other linguistic attributes. QQ serves both as (1) a simple "glue" library in multilingual NLP research to make working with many languages easier, and (2) as an intuitive way for exploring languages, such as finding related ones through shared scripts, regions or other metadata.

Problem

• language identifiers
• multilingual NLP
• metadata management
• language standardization

Innovation

• language identifiers
• metadata management
• multilingual NLP
• graph-based language representation
• identifier normalization