Telenor Nordics Customer Service self-help corpus
Not on the map. Why?
Abstract (arXiv)
This paper presents a multilingual customer service self-help corpus comprising 1,122 manually validated documents in Finnish, Danish, Norwegian, and Swedish, totaling 274,599 words and 1,884,833 characters. The documents have been sourced from the public self-help pages of four Nordic telecommunications operators and subsequently filtered for person-identifiable information and relevance through a combined LLM and human annotation pipeline. Domain-specific datasets for Nordic languages remain scarce, particularly in customer service: a domain of growing importance for retrieval-augmented generation, cross-lingual transfer learning, and emerging agent-based service architectures. An analysis of the corpus reveals substantial variation in document length and structure across operators, reflecting distinct editorial strategies, as well as broad topical coverage spanning network hardware, mobile services, TV and streaming, billing, and account management. The dataset is publicly available under a CC-BY-NC-SA-4.0 license at https://zenodo.org/records/20732652, intended to support reproducible research in Nordic NLP and information retrieval.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 4 of its 18 references that are in the map. First posted on arXiv 26 May 2026.
Builds on
4 of 18 references.
References · 4
- 2018Datasheets for Datasetscited by 1,386
- 2024Multilingual E5 Text Embeddings: A Technical Reportcited by 372
- 2025Just Put a Human in the Loop? Investigating LLM-Assisted Annotation for Subjective Taskscited by 21
- 2025WixQA: A Multi-Dataset Benchmark for Enterprise Retrieval-Augmented Generationcited by 10
Built on by
Not cited yet.
Similar, not cited
Close in wording, no citation either way
- 2025Building a Few-Shot Cross-Domain Multilingual NLU Model for Customer Care
- 2026Can Small Language Models Handle Context-Summarized Multi-Turn Customer-Service QA? A Synthetic Data-Driven Comparative Evaluation
- 2023The economic trade-offs of large language models: A case study
- 2025Evaluating Machine Translation Datasets for Low-Web Data Languages: A Gendered Lens
- 2024Retrieval-Augmented Generation with Knowledge Graphs for Customer Service Question Answering
- 2026LLM-based Models for Detecting Emerging Topics in Service Feedback
- 2025CAPRAG: A Large Language Model Solution for Customer Service and Automatic Reporting using Vector and Graph Retrieval-Augmented Generation
- 2024HR-MultiWOZ: A Task Oriented Dialogue (TOD) Dataset for HR LLM Agent