Information Dropping Data Augmentation for Machine Translation Quality Estimation-Reference-Cited by-同舟云学术

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Information Dropping Data Augmentation for Machine Translation Quality Estimation

Published:2024 Issue: Volume:32 Page:2112-2124
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:

Li Shuo¹^ORCID,Bi Xiaojun²^ORCID,Liu Tao³^ORCID,Chen Zheng²^ORCID

Affiliation:

1. College of Information and Communication Engineering, Harbin Engineering University, Harbin, China

2. Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance of MOE, Minzu University of China, Beijing, China

3. College of Computer Science and Technology, Harbin Engineering University, Harbin, China

Funder

National Natural Science Foundation of China

National Social Science Fund of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6570655/10304349/10478185.pdf?arnumber=10478185

Reference60 articles.

1. UScore: An Effective Approach to Fully Unsupervised Evaluation Metrics for Machine Translation

2. BLEU

3. Machine translation evaluation versus quality estimation

4. MLQE-PE: A multilingual quality estimation and post-editing dataset;Fomicheva,2022

5. Improving Translation Quality Estimation with Bias Mitigation

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1. Fine-tuning inflow prediction models: integrating optimization algorithms and TRMM data for enhanced accuracy;Water Science & Technology;2024-07-03

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