@inproceedings{ali-etal-2023-gari,
title = "{GARI}: Graph Attention for Relative Isomorphism of {A}rabic Word Embeddings",
author = "Ali, Muhammad and
Alshmrani, Maha and
Qin, Jianbin and
Hu, Yan and
Wang, Di",
editor = "Sawaf, Hassan and
El-Beltagy, Samhaa and
Zaghouani, Wajdi and
Magdy, Walid and
Abdelali, Ahmed and
Tomeh, Nadi and
Abu Farha, Ibrahim and
Habash, Nizar and
Khalifa, Salam and
Keleg, Amr and
Haddad, Hatem and
Zitouni, Imed and
Mrini, Khalil and
Almatham, Rawan",
booktitle = "Proceedings of ArabicNLP 2023",
month = dec,
year = "2023",
address = "Singapore (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.arabicnlp-1.16",
doi = "10.18653/v1/2023.arabicnlp-1.16",
pages = "181--190",
abstract = "Bilingual Lexical Induction (BLI) is a core challenge in NLP, it relies on the relative isomorphism of individual embedding spaces. Existing attempts aimed at controlling the relative isomorphism of different embedding spaces fail to incorporate the impact of semantically related words in the model training objective. To address this, we propose GARI that combines the distributional training objectives with multiple isomorphism losses guided by the graph attention network. GARI considers the impact of semantical variations of words in order to define the relative isomorphism of the embedding spaces. Experimental evaluation using the Arabic language data set shows that GARI outperforms the existing research by improving the average P@1 by a relative score of up to 40.95{\%} and 76.80{\%} for in-domain and domain mismatch settings respectively.",
}
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%0 Conference Proceedings
%T GARI: Graph Attention for Relative Isomorphism of Arabic Word Embeddings
%A Ali, Muhammad
%A Alshmrani, Maha
%A Qin, Jianbin
%A Hu, Yan
%A Wang, Di
%Y Sawaf, Hassan
%Y El-Beltagy, Samhaa
%Y Zaghouani, Wajdi
%Y Magdy, Walid
%Y Abdelali, Ahmed
%Y Tomeh, Nadi
%Y Abu Farha, Ibrahim
%Y Habash, Nizar
%Y Khalifa, Salam
%Y Keleg, Amr
%Y Haddad, Hatem
%Y Zitouni, Imed
%Y Mrini, Khalil
%Y Almatham, Rawan
%S Proceedings of ArabicNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore (Hybrid)
%F ali-etal-2023-gari
%X Bilingual Lexical Induction (BLI) is a core challenge in NLP, it relies on the relative isomorphism of individual embedding spaces. Existing attempts aimed at controlling the relative isomorphism of different embedding spaces fail to incorporate the impact of semantically related words in the model training objective. To address this, we propose GARI that combines the distributional training objectives with multiple isomorphism losses guided by the graph attention network. GARI considers the impact of semantical variations of words in order to define the relative isomorphism of the embedding spaces. Experimental evaluation using the Arabic language data set shows that GARI outperforms the existing research by improving the average P@1 by a relative score of up to 40.95% and 76.80% for in-domain and domain mismatch settings respectively.
%R 10.18653/v1/2023.arabicnlp-1.16
%U https://aclanthology.org/2023.arabicnlp-1.16
%U https://doi.org/10.18653/v1/2023.arabicnlp-1.16
%P 181-190
Markdown (Informal)
[GARI: Graph Attention for Relative Isomorphism of Arabic Word Embeddings](https://aclanthology.org/2023.arabicnlp-1.16) (Ali et al., ArabicNLP-WS 2023)
ACL