@inproceedings{klein-etal-2017-improving,
title = "Improving Neural Knowledge Base Completion with Cross-Lingual Projections",
author = "Klein, Patrick and
Ponzetto, Simone Paolo and
Glava{\v{s}}, Goran",
editor = "Lapata, Mirella and
Blunsom, Phil and
Koller, Alexander",
booktitle = "Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 2, Short Papers",
month = apr,
year = "2017",
address = "Valencia, Spain",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/E17-2083",
pages = "516--522",
abstract = "In this paper we present a cross-lingual extension of a neural tensor network model for knowledge base completion. We exploit multilingual synsets from BabelNet to translate English triples to other languages and then augment the reference knowledge base with cross-lingual triples. We project monolingual embeddings of different languages to a shared multilingual space and use them for network initialization (i.e., as initial concept embeddings). We then train the network with triples from the cross-lingually augmented knowledge base. Results on WordNet link prediction show that leveraging cross-lingual information yields significant gains over exploiting only monolingual triples.",
}
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%0 Conference Proceedings
%T Improving Neural Knowledge Base Completion with Cross-Lingual Projections
%A Klein, Patrick
%A Ponzetto, Simone Paolo
%A Glavaš, Goran
%Y Lapata, Mirella
%Y Blunsom, Phil
%Y Koller, Alexander
%S Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers
%D 2017
%8 April
%I Association for Computational Linguistics
%C Valencia, Spain
%F klein-etal-2017-improving
%X In this paper we present a cross-lingual extension of a neural tensor network model for knowledge base completion. We exploit multilingual synsets from BabelNet to translate English triples to other languages and then augment the reference knowledge base with cross-lingual triples. We project monolingual embeddings of different languages to a shared multilingual space and use them for network initialization (i.e., as initial concept embeddings). We then train the network with triples from the cross-lingually augmented knowledge base. Results on WordNet link prediction show that leveraging cross-lingual information yields significant gains over exploiting only monolingual triples.
%U https://aclanthology.org/E17-2083
%P 516-522
Markdown (Informal)
[Improving Neural Knowledge Base Completion with Cross-Lingual Projections](https://aclanthology.org/E17-2083) (Klein et al., EACL 2017)
ACL