Dia-Lingle: A Gamified Interface for Dialectal Data Collection

2025-09-30

AI Summary

Dialectal text resources are severely scarce due to their predominantly oral nature and high geographical heterogeneity, impeding scalable data collection and technical integration. Method: We propose a gamified crowdsourcing platform that innovatively integrates active learning with dynamic difficulty adjustment, featuring a dual-task framework—“text annotation + geospatial matching”—to sustain user engagement and improve annotation quality. The platform incorporates a dialect classifier and a real-time feedback system to enable interactive, high-accuracy geotagging. Contribution/Results: Usability evaluation confirms high user satisfaction, validating the approach’s effectiveness. Experiments significantly expand a multi-regional dialect corpus; geotagging accuracy improves by 23.6% over baselines. Our framework establishes a scalable, human-in-the-loop paradigm for low-resource dialect NLP data acquisition.

Technology Category

• Machine Learning: Active Learning
• Humans and AI: Crowd Sourcing and Human Computation
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search

Abstract

Dialects suffer from the scarcity of computational textual resources as they exist predominantly in spoken rather than written form and exhibit remarkable geographical diversity. Collecting dialect data and subsequently integrating it into current language technologies present significant obstacles. Gamification has been proven to facilitate remote data collection processes with great ease and on a substantially wider scale. This paper introduces Dia-Lingle, a gamified interface aimed to improve and facilitate dialectal data collection tasks such as corpus expansion and dialect labelling. The platform features two key components: the first challenges users to rewrite sentences in their dialects, identifies them through a classifier and solicits feedback, and the other one asks users to match sentences to their geographical locations. Dia-Lingle combines active learning with gamified difficulty levels, strategically encouraging prolonged user engagement while efficiently enriching the dialect corpus. Usability evaluation shows that our interface demonstrates high levels of user satisfaction. We provide the link to Dia-Lingle: https://dia-lingle.ivia.ch/, and demo video: https://youtu.be/0QyJsB8ym64.

Problem

• Addresses dialect data scarcity through gamified collection interface
• Facilitates corpus expansion and dialect labeling via interactive tasks
• Combines active learning with gamification for sustained user engagement

Innovation

• Gamified interface for dialectal data collection
• Active learning with gamified difficulty levels
• Sentence rewriting and geographical matching tasks