A Novel Unsupervised Approach for Cross-Lingual Word Alignment in Low Isomorphic Embedding Spaces-Reference-Cited by-同舟云学术

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A Novel Unsupervised Approach for Cross-Lingual Word Alignment in Low Isomorphic Embedding Spaces

Published:2023 Issue: Volume:31 Page:3027-3041
ISSN:2329-9290
Container-title:IEEE/ACM Transactions on Audio, Speech, and Language Processing
language:
Short-container-title:IEEE/ACM Trans. Audio Speech Lang. Process.

Author:

Tao Qian¹^ORCID,Xiong Zhihao²,Han Bocheng¹^ORCID,Fan Xiaoyang¹^ORCID,Li Lusi³^ORCID

Affiliation:

1. Software Service Engineering and Cloud Computing Laboratory, School of Software, South China University of Technology, Guangzhou, China

2. Commerce Strategy Platform Department, Baidu Inc., Beijing, China

3. Department of Computer Science, Old Dominion University, Norfolk, VA, USA

Funder

National Natural Science Foundation of China

National Key R&D Program of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Acoustics and Ultrasonics,Computer Science (miscellaneous),Computational Mathematics

Link

http://xplorestaging.ieee.org/ielx7/6570655/9970249/10202195.pdf?arnumber=10202195

Reference51 articles.

1. Generalizing and Improving Bilingual Word Embedding Mappings with a Multi-Step Framework of Linear Transformations

2. Learning bilingual word embeddings with (almost) no bilingual data

3. Classification-Based Self-Learning for Weakly Supervised Bilingual Lexicon Induction

4. Loss in Translation: Learning Bilingual Word Mapping with a Retrieval Criterion

5. Exploiting similarities among languages for machine translation;mikolov,2013

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