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.- Anthology ID:
- E17-2083
- Volume:
- Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers
- Month:
- April
- Year:
- 2017
- Address:
- Valencia, Spain
- Venue:
- EACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 516–522
- Language:
- URL:
- https://aclanthology.org/E17-2083
- DOI:
- Cite (ACL):
- Patrick Klein, Simone Paolo Ponzetto, and Goran Glavaš. 2017. Improving Neural Knowledge Base Completion with Cross-Lingual Projections. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, pages 516–522, Valencia, Spain. Association for Computational Linguistics.
- Cite (Informal):
- Improving Neural Knowledge Base Completion with Cross-Lingual Projections (Klein et al., EACL 2017)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/E17-2083.pdf