Dynaword: From One-shot to Continuously Developed Datasets

TL;DR

Dynaword introduces a framework and implementation for creating large, openly licensed NLP datasets that are continuously updated through community collaboration, enhancing dataset quality, size, and longevity.

Contribution

The paper presents the Dynaword framework and Danish Dynaword implementation, enabling ongoing community-driven dataset development in NLP.

Findings

Danish Dynaword has over four times more tokens than comparable datasets.

It is exclusively openly licensed, facilitating sharing and derivative works.

The dataset includes quality assurance tests for data formatting and documentation.

Abstract

Large-scale datasets are foundational for research and development in natural language processing. However, current approaches face three key challenges: (1) reliance on ambiguously licensed sources restricting use, sharing, and derivative works; (2) static dataset releases that prevent community contributions and diminish longevity; and (3) quality assurance processes restricted to publishing teams rather than leveraging community expertise. To address these limitations, we introduce two contributions: the Dynaword approach and Danish Dynaword. The Dynaword approach is a framework for creating large-scale, open datasets that can be continuously updated through community collaboration. Danish Dynaword is a concrete implementation that validates this approach and demonstrates its potential. Danish Dynaword contains over four times as many tokens as comparable releases, is exclusively openly licensed, and has received multiple contributions across industry and research. The repository includes light-weight tests to ensure data formatting, quality, and documentation, establishing a sustainable framework for ongoing community contributions and dataset evolution.