Adapting Entities across Languages and Cultures

Denis Peskov, Viktor Hangya, Jordan Boyd-Graber, Alexander Fraser


Abstract
How would you explain Bill Gates to a German? He is associated with founding a company in the United States, so perhaps the German founder Carl Benz could stand in for Gates in those contexts. This type of translation is called adaptation in the translation community. Until now, this task has not been done computationally. Automatic adaptation could be used in natural language processing for machine translation and indirectly for generating new question answering datasets and education. We propose two automatic methods and compare them to human results for this novel NLP task. First, a structured knowledge base adapts named entities using their shared properties. Second, vector-arithmetic and orthogonal embedding mappings methods identify better candidates, but at the expense of interpretable features. We evaluate our methods through a new dataset of human adaptations.
Anthology ID:
2021.findings-emnlp.315
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2021
Month:
November
Year:
2021
Address:
Punta Cana, Dominican Republic
Editors:
Marie-Francine Moens, Xuanjing Huang, Lucia Specia, Scott Wen-tau Yih
Venue:
Findings
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
3725–3750
Language:
URL:
https://aclanthology.org/2021.findings-emnlp.315
DOI:
10.18653/v1/2021.findings-emnlp.315
Bibkey:
Cite (ACL):
Denis Peskov, Viktor Hangya, Jordan Boyd-Graber, and Alexander Fraser. 2021. Adapting Entities across Languages and Cultures. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 3725–3750, Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
Adapting Entities across Languages and Cultures (Peskov et al., Findings 2021)
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PDF:
https://aclanthology.org/2021.findings-emnlp.315.pdf
Video:
 https://aclanthology.org/2021.findings-emnlp.315.mp4