@inproceedings{chiarcos-etal-2020-translation,
title = "Translation Inference by Concept Propagation",
author = {Chiarcos, Christian and
Schenk, Niko and
F{\"a}th, Christian},
editor = "Kernerman, Ilan and
Krek, Simon and
McCrae, John P. and
Gracia, Jorge and
Ahmadi, Sina and
Kabashi, Besim",
booktitle = "Proceedings of the 2020 Globalex Workshop on Linked Lexicography",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2020.globalex-1.16",
pages = "98--105",
abstract = "This paper describes our contribution to the Third Shared Task on Translation Inference across Dictionaries (TIAD-2020). We describe an approach on translation inference based on symbolic methods, the propagation of concepts over a graph of interconnected dictionaries: Given a mapping from source language words to lexical concepts (e.g., synsets) as a seed, we use bilingual dictionaries to extrapolate a mapping of pivot and target language words to these lexical concepts. Translation inference is then performed by looking up the lexical concept(s) of a source language word and returning the target language word(s) for which these lexical concepts have the respective highest score. We present two instantiations of this system: One using WordNet synsets as concepts, and one using lexical entries (translations) as concepts. With a threshold of 0, the latter configuration is the second among participant systems in terms of F1 score. We also describe additional evaluation experiments on Apertium data, a comparison with an earlier approach based on embedding projection, and an approach for constrained projection that outperforms the TIAD-2020 vanilla system by a large margin.",
language = "English",
ISBN = "979-10-95546-46-7",
}
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<abstract>This paper describes our contribution to the Third Shared Task on Translation Inference across Dictionaries (TIAD-2020). We describe an approach on translation inference based on symbolic methods, the propagation of concepts over a graph of interconnected dictionaries: Given a mapping from source language words to lexical concepts (e.g., synsets) as a seed, we use bilingual dictionaries to extrapolate a mapping of pivot and target language words to these lexical concepts. Translation inference is then performed by looking up the lexical concept(s) of a source language word and returning the target language word(s) for which these lexical concepts have the respective highest score. We present two instantiations of this system: One using WordNet synsets as concepts, and one using lexical entries (translations) as concepts. With a threshold of 0, the latter configuration is the second among participant systems in terms of F1 score. We also describe additional evaluation experiments on Apertium data, a comparison with an earlier approach based on embedding projection, and an approach for constrained projection that outperforms the TIAD-2020 vanilla system by a large margin.</abstract>
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%0 Conference Proceedings
%T Translation Inference by Concept Propagation
%A Chiarcos, Christian
%A Schenk, Niko
%A Fäth, Christian
%Y Kernerman, Ilan
%Y Krek, Simon
%Y McCrae, John P.
%Y Gracia, Jorge
%Y Ahmadi, Sina
%Y Kabashi, Besim
%S Proceedings of the 2020 Globalex Workshop on Linked Lexicography
%D 2020
%8 May
%I European Language Resources Association
%C Marseille, France
%@ 979-10-95546-46-7
%G English
%F chiarcos-etal-2020-translation
%X This paper describes our contribution to the Third Shared Task on Translation Inference across Dictionaries (TIAD-2020). We describe an approach on translation inference based on symbolic methods, the propagation of concepts over a graph of interconnected dictionaries: Given a mapping from source language words to lexical concepts (e.g., synsets) as a seed, we use bilingual dictionaries to extrapolate a mapping of pivot and target language words to these lexical concepts. Translation inference is then performed by looking up the lexical concept(s) of a source language word and returning the target language word(s) for which these lexical concepts have the respective highest score. We present two instantiations of this system: One using WordNet synsets as concepts, and one using lexical entries (translations) as concepts. With a threshold of 0, the latter configuration is the second among participant systems in terms of F1 score. We also describe additional evaluation experiments on Apertium data, a comparison with an earlier approach based on embedding projection, and an approach for constrained projection that outperforms the TIAD-2020 vanilla system by a large margin.
%U https://aclanthology.org/2020.globalex-1.16
%P 98-105
Markdown (Informal)
[Translation Inference by Concept Propagation](https://aclanthology.org/2020.globalex-1.16) (Chiarcos et al., GLOBALEX 2020)
ACL
- Christian Chiarcos, Niko Schenk, and Christian Fäth. 2020. Translation Inference by Concept Propagation. In Proceedings of the 2020 Globalex Workshop on Linked Lexicography, pages 98–105, Marseille, France. European Language Resources Association.