@inproceedings{han-etal-2023-bridging,
title = "Bridging Background Knowledge Gaps in Translation with Automatic Explicitation",
author = "Han, HyoJung and
Boyd-Graber, Jordan and
Carpuat, Marine",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-main.603",
doi = "10.18653/v1/2023.emnlp-main.603",
pages = "9718--9735",
abstract = "Translations help people understand content written in another language. However, even correct literal translations do not fulfill that goal when people lack the necessary background to understand them. Professional translators incorporate explicitations to explain the missing context by considering cultural differences between source and target audiences. Despite its potential to help users, NLP research on explicitation is limited because of the dearth of adequate evaluation methods. This work introduces techniques for automatically generating explicitations, motivated by WikiExpl: a dataset that we collect from Wikipedia and annotate with human translators. The resulting explicitations are useful as they help answer questions more accurately in a multilingual question answering framework.",
}
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%0 Conference Proceedings
%T Bridging Background Knowledge Gaps in Translation with Automatic Explicitation
%A Han, HyoJung
%A Boyd-Graber, Jordan
%A Carpuat, Marine
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F han-etal-2023-bridging
%X Translations help people understand content written in another language. However, even correct literal translations do not fulfill that goal when people lack the necessary background to understand them. Professional translators incorporate explicitations to explain the missing context by considering cultural differences between source and target audiences. Despite its potential to help users, NLP research on explicitation is limited because of the dearth of adequate evaluation methods. This work introduces techniques for automatically generating explicitations, motivated by WikiExpl: a dataset that we collect from Wikipedia and annotate with human translators. The resulting explicitations are useful as they help answer questions more accurately in a multilingual question answering framework.
%R 10.18653/v1/2023.emnlp-main.603
%U https://aclanthology.org/2023.emnlp-main.603
%U https://doi.org/10.18653/v1/2023.emnlp-main.603
%P 9718-9735
Markdown (Informal)
[Bridging Background Knowledge Gaps in Translation with Automatic Explicitation](https://aclanthology.org/2023.emnlp-main.603) (Han et al., EMNLP 2023)
ACL