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

arXiv: https://arxiv.org/abs/2605.12933 · HTML: https://mlsanity.com/paper/2605.12933/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 30 references that are in the map.
- First posted on arXiv 13 May 2026.

## Builds on (most cited first, 10 of 10)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Gemma 2: Improving Open Language Models at a Practical Size](https://mlsanity.com/paper/2408.00118/) (2024)
- [A Call for Clarity in Reporting BLEU Scores](https://mlsanity.com/paper/1804.08771/) (2018)
- [Multilingual Denoising Pre-training for Neural Machine Translation](https://mlsanity.com/paper/2001.08210/) (2020)
- [Global MMLU: Understanding and Addressing Cultural and Linguistic Biases in Multilingual Evaluation](https://mlsanity.com/paper/2412.03304/) (2024)
- [A Pragmatic Guide to Geoparsing Evaluation](https://mlsanity.com/paper/1810.12368/) (2018)
- [Location reference recognition from texts: A survey and comparison](https://mlsanity.com/paper/2207.01683/) (2022)
- [Arukikata Travelogue Dataset with Geographic Entity Mention, Coreference, and Link Annotation](https://mlsanity.com/paper/2305.13844/) (2023)
- [Multi-lingual Geoparsing based on Machine Translation](https://mlsanity.com/paper/1511.01974/) (2015)

## 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.