O'Keefe, Simon orcid.org/0000-0001-5957-2474, Alqaisi, Taghreed and Komninos, Alexandros (2020) Dependency Based Bilingual word Embeddings without word alignment. In: 2020 International Joint Conference on Neural Networks (IJCNN).
Abstract
In this work, we trained different bilingual word embeddings models without word alignments (BilBOWA) using linear Bag-of-words contexts and dependency-based contexts. BilBOWA embedding models learn distributed representations of words by jointly optimizing a monolingual and a bilingual objective. Including dependency features in the monolingual objective, improves the accuracy of learning bilingual word embeddings up to 6% points in English-Spanish (En-Es) and up to 2.5% points in English-German (En-De) language pairs in word translation task compared to the baseline model. However, using these dependency features in both monolingual and bilingual objectives does not lead to any improvement in the En-Es language pair and only shows minor improvement for En-De. Moreover, our results provide evidence that using dependency features in bilingual word embeddings has a different effect based on syntactic and sentence structure similarity of the language pair.
Metadata
Item Type: | Proceedings Paper |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © IEEE, 2020. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details |
Dates: |
|
Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Computer Science (York) |
Depositing User: | Pure (York) |
Date Deposited: | 25 Nov 2021 12:10 |
Last Modified: | 09 Jan 2025 00:14 |
Published Version: | https://doi.org/10.1109/IJCNN48605.2020.9206732 |
Status: | Published |
Identification Number: | 10.1109/IJCNN48605.2020.9206732 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:180866 |