Translation inference through multi-lingual word embedding similarity

  • This paper describes our contribution to the Shared Task on Translation Inference across Dictionaries (TIAD-2019). In our approach, we construct a multi-lingual word embedding space by projecting new languages in the feature space of a language for which a pretrained embedding model exists. We use the similarity of the word embeddings to predict candidate translations. Even if our projection methodology is rather simplistic, our system outperforms the other participating systems with respect to the F1 measure for the language pairs which we predicted.

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Metadaten
Author:Kathrin Donandt, Christian ChiarcosORCiDGND
URN:urn:nbn:de:bvb:384-opus4-1040754
Frontdoor URLhttps://opus.bibliothek.uni-augsburg.de/opus4/104075
URL:https://ceur-ws.org/Vol-2493/
Parent Title (English):TIAD 2019 - TIAD-2019 Shared Task - Translation Inference Across Dictionaries: Proceedings of TIAD-2019 Shared Task – Translation Inference Across Dictionaries co-located with the 2nd Language, Data and Knowledge Conference (LDK 2019), Leipzig, Germany, May 20, 2019
Publisher:CEUR-WS
Place of publication:Aachen
Editor:Jorge Garcia, Besim Kabashi, Ilan Kernerman
Type:Conference Proceeding
Language:English
Year of first Publication:2019
Publishing Institution:Universität Augsburg
Release Date:2023/05/15
First Page:42
Last Page:53
Series:CEUR Workshop Proceedings ; 2493
Institutes:Philologisch-Historische Fakultät
Philologisch-Historische Fakultät / Angewandte Computerlinguistik
Philologisch-Historische Fakultät / Angewandte Computerlinguistik / Lehrstuhl für Angewandte Computerlinguistik (ACoLi)
Dewey Decimal Classification:4 Sprache / 40 Sprache / 400 Sprache
Licence (German):CC-BY 4.0: Creative Commons: Namensnennung (mit Print on Demand)