Abstract
Automated construction of bilingual dictionaries using monolingual embedding spaces is a core challenge in machine translation. The end performance of these dictionaries relies upon the geometric similarity of individual spaces, i.e., their degree of isomorphism. Existing attempts aimed at controlling the relative isomorphism of different spaces fail to incorporate the impact of semantically related words in the training objective. To address this, we propose GRI that combines the distributional training objectives with attentive graph convolutions to unanimously consider the impact of semantically similar words required to define/compute the relative isomorphism of multiple spaces. Experimental evaluation shows that GRI outperforms the existing research by improving the average P@1 by a relative score of up to 63.6%. We release the codes for GRI at https://github.com/asif6827/GRI.
| Original language | English (US) |
|---|---|
| Title of host publication | Findings of the Association for Computational Linguistics |
| Subtitle of host publication | EMNLP 2023 |
| Publisher | Association for Computational Linguistics (ACL) |
| Pages | 11304-11313 |
| Number of pages | 10 |
| ISBN (Electronic) | 9798891760615 |
| State | Published - 2023 |
| Event | 2023 Findings of the Association for Computational Linguistics: EMNLP 2023 - Singapore, Singapore Duration: Dec 6 2023 → Dec 10 2023 |
Publication series
| Name | Findings of the Association for Computational Linguistics: EMNLP 2023 |
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Conference
| Conference | 2023 Findings of the Association for Computational Linguistics: EMNLP 2023 |
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| Country/Territory | Singapore |
| City | Singapore |
| Period | 12/6/23 → 12/10/23 |
Bibliographical note
Publisher Copyright:© 2023 Association for Computational Linguistics.
ASJC Scopus subject areas
- Computational Theory and Mathematics
- Computer Science Applications
- Information Systems
- Language and Linguistics
- Linguistics and Language