@article{haoyu2026,
title = {From Synthetic to Localized: Benchmarking Multilingual Intent Classification in Logistics Customer Service},
author = {He, Haoyu and Zhuang, Jinyu and Chu, Haoran and yu, Shuhang and Wang, Hao and Kunpeng, Han},
journal = {Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track},
year = {2026},
month = {Jan},
doi = {10.18653/2026.emnlp-industry.121},
url = {https://doi.org/10.18653/2026.emnlp-industry.121},
}
He, Haoyu et al. (2026). From Synthetic to Localized: Benchmarking Multilingual Intent Classification in Logistics Customer Service. Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track. https://doi.org/10.18653/2026.emnlp-industry.121
He, Haoyu, et al.. "From Synthetic to Localized: Benchmarking Multilingual Intent Classification in Logistics Customer Service." Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track, 2026, https://doi.org/10.18653/2026.emnlp-industry.121.
He, Haoyu, et al.. "From Synthetic to Localized: Benchmarking Multilingual Intent Classification in Logistics Customer Service." Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track (2026). https://doi.org/10.18653/2026.emnlp-industry.121
He, Haoyu et al., 2026. From Synthetic to Localized: Benchmarking Multilingual Intent Classification in Logistics Customer Service. Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track. Available at: <https://doi.org/10.18653/2026.emnlp-industry.121>.
TY - JOUR
TI - From Synthetic to Localized: Benchmarking Multilingual Intent Classification in Logistics Customer Service
AU - He, Haoyu
AU - Zhuang, Jinyu
AU - Chu, Haoran
AU - yu, Shuhang
AU - Wang, Hao
AU - Kunpeng, Han
PY - 2026
JO - Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track
DO - 10.18653/2026.emnlp-industry.121
UR - https://doi.org/10.18653/2026.emnlp-industry.121
ER -