mlsanityData to 7 Oct 2026

ATD-Trans: A Geographically Grounded Japanese-English Travelogue Translation Dataset

arXiv 2605.12933 ↗First posted 13 May 2026cs.CLcited by 0

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Abstract (arXiv)

Geographic text, or textual data rich in geographic (geo-) information is a valuable source for various geographic applications, e.g., tourism management. Making such information accessible to speakers of other languages further enhances its utility; thus, accurate machine translation (MT) is essential for equity in multilingual geo-information access. To facilitate in-depth analysis for geographic text, we introduce ATD-Trans, a geographically grounded Japanese--English travelogue translation dataset, which enables evaluation of MT quality at both the overall and geo-entity levels across domestic (within Japan) and overseas regions. Our experiments on existing language models examine two factors: model language focus and geographic regions. The results highlight advantages of Japanese-enhanced models and greater difficulty in translating domestic-region geo-entities mentioned in travel blogs.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 10 of its 30 references that are in the map. First posted on arXiv 13 May 2026.

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References · 10

  1. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
  2. 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,907
  3. 2024Gemma 2: Improving Open Language Models at a Practical Sizecited by 1,928
  4. 2018A Call for Clarity in Reporting BLEU Scorescited by 1,790
  5. 2020Multilingual Denoising Pre-training for Neural Machine Translationcited by 1,028
  6. 2024Global MMLU: Understanding and Addressing Cultural and Linguistic Biases in Multilingual Evaluationcited by 181
  7. 2018A Pragmatic Guide to Geoparsing Evaluationcited by 17
  8. 2022Location reference recognition from texts: A survey and comparisoncited by 17
  9. 2023Arukikata Travelogue Dataset with Geographic Entity Mention, Coreference, and Link Annotationcited by 2
  10. 2015Multi-lingual Geoparsing based on Machine Translationcited by 1

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