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Towards a Universal Semantic Dictionary
Version 1
: Received: 25 July 2019 / Approved: 29 July 2019 / Online: 29 July 2019 (11:05:16 CEST)
A peer-reviewed article of this Preprint also exists.
Castro-Bleda, M.J.; Iklódi, E.; Recski, G.; Borbély, G. Towards a Universal Semantic Dictionary. Appl. Sci. 2019, 9, 4060. Castro-Bleda, M.J.; Iklódi, E.; Recski, G.; Borbély, G. Towards a Universal Semantic Dictionary. Appl. Sci. 2019, 9, 4060.
DOI: 10.3390/app9194060
Abstract
A novel method for finding linear mappings among word embeddings for several languages, taking as pivot a shared, universal embedding space, is proposed in this paper. Previous approaches learn translation matrices between two specific languages, but this method learn translation matrices between a given language and a shared, universal space. The system was first trained on bilingual, and later on multilingual corpora as well. In the first case two different training data were applied; Dinu’s English-Italian benchmark data, and English-Italian translation pairs extracted from the PanLex database. In the second case only the PanLex database was used. The system performs on English-Italian languages with the best setting significantly better than the baseline system of Mikolov et al. [1], and it provides a comparable performance with the more sophisticated systems of Faruqui and Dyer [2] and Dinu et al. [3]. Exploiting the richness of the PanLex database, the proposed method makes it possible to learn linear mappings among an arbitrary number of languages.
Keywords
natural language processing; semantics; word embeddings; multilingual embeddings; translation; artificial neural networks
Subject
MATHEMATICS & COMPUTER SCIENCE, Artificial Intelligence & Robotics
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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