Translation Inference by Concept Propagation

Christian Chiarcos, Niko Schenk, Christian Fäth

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.
Anthology ID:
2020.globalex-1.16
Volume:
Proceedings of the 2020 Globalex Workshop on Linked Lexicography
Month:
May
Year:
2020
Address:
Marseille, France
Editors:
Ilan Kernerman, Simon Krek, John P. McCrae, Jorge Gracia, Sina Ahmadi, Besim Kabashi
Venue:
GLOBALEX
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
98–105
Language:
English
URL:
https://aclanthology.org/2020.globalex-1.16
DOI:
Bibkey:
Cite (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.
Cite (Informal):
Translation Inference by Concept Propagation (Chiarcos et al., GLOBALEX 2020)
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PDF:
https://preview.aclanthology.org/emnlp22-frontmatter/2020.globalex-1.16.pdf
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