Telenor Nordics Customer Service self-help corpus

2026-06-19 · Source: cs.CL updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Natural Language Processing · Depth: Expert, long

Summary

The Telenor Nordics Customer Service Self-Help Corpus is a new multilingual dataset containing 1,122 manually validated documents in Finnish, Danish, Norwegian, and Swedish, totaling 1,041,612 tokens. Sourced from the public self-help pages of four Nordic telecommunications operators (Telenor Denmark, Telenor Norway, Telenor Sweden, and DNA Finland), the corpus was processed via a pipeline combining LLM (Gemma-3-27b-it) pre-annotation and human validation to remove Person-Identifiable Information and ensure relevance. The data, scraped on 23/05/2025, is publicly available under a CC-BY-NC-SA-4.0 license. Analysis shows significant variation in document length and structure across operators, with Finnish and Norwegian documents being considerably longer. Topical coverage is broad, including network hardware (33%), mobile services, and TV/streaming.

Key takeaway

For NLP Engineers developing customer service solutions for Nordic markets, this new corpus offers a crucial, ethically sourced resource. You can utilize its multilingual, real-world data to build and evaluate retrieval-augmented generation systems, conduct cross-lingual transfer learning, or benchmark embedding models. Be aware of the dataset's static nature and potential annotation bias, and consider the unbalanced language distribution for robust per-language evaluation.

Key insights

A new, ethically sourced, multilingual customer service self-help corpus for Nordic languages addresses a critical data scarcity for NLP research.

Principles

• Domain-specific datasets are scarce for Nordic NLP.
• Combined LLM and human annotation improves data quality.
• Real-world data exhibits significant structural variation.

Method

Data preparation involves web scraping, HTML to Markdown conversion, LLM (Gemma-3-27b-it) pre-annotation for relevance/PII/span, LLM translation to English, human validation, and final filtering based on criteria.

In practice

• Use corpus for RAG knowledge base evaluation.
• Apply for cross-lingual transfer learning experiments.
• Benchmark embedding models for Nordic languages.

Topics

• Telenor Nordics
• Customer Service NLP
• Multilingual Datasets
• Retrieval-Augmented Generation
• LLM Annotation
• Nordic Languages

Code references

• tnresearch/tn_selfhelp_corpus

Best for: Research Scientist, AI Scientist, NLP Engineer, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CL updates on arXiv.org.