@inproceedings{peskov-etal-2021-adapting-entities,
title = "Adapting Entities across Languages and Cultures",
author = "Peskov, Denis and
Hangya, Viktor and
Boyd-Graber, Jordan and
Fraser, Alexander",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.findings-emnlp.315",
doi = "10.18653/v1/2021.findings-emnlp.315",
pages = "3725--3750",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Adapting Entities across Languages and Cultures
%A Peskov, Denis
%A Hangya, Viktor
%A Boyd-Graber, Jordan
%A Fraser, Alexander
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Findings of the Association for Computational Linguistics: EMNLP 2021
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F peskov-etal-2021-adapting-entities
%X 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.
%R 10.18653/v1/2021.findings-emnlp.315
%U https://aclanthology.org/2021.findings-emnlp.315
%U https://doi.org/10.18653/v1/2021.findings-emnlp.315
%P 3725-3750
Markdown (Informal)
[Adapting Entities across Languages and Cultures](https://aclanthology.org/2021.findings-emnlp.315) (Peskov et al., Findings 2021)
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.