Word-Level Quality Estimation for Korean-English Neural Machine Translation
Quality estimation (QE) task aims to predict the machine translation (MT) quality well by referring to the source sentence and its MT output. The various applicability of QE proves the importance of QE research, but the enormous human labor to construct the QE dataset remains a challenge. This study...
Main Authors: | Sugyeong Eo, Chanjun Park, Hyeonseok Moon, Jaehyung Seo, Heuiseok Lim |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2022-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/9761258/ |
Similar Items
-
Comparative Analysis of Current Approaches to Quality Estimation for Neural Machine Translation
by: Sugyeong Eo, et al.
Published: (2021-07-01) -
An Empirical Study on Automatic Post Editing for Neural Machine Translation
by: Hyeonseok Moon, et al.
Published: (2021-01-01) -
Plain Template Insertion: Korean-Prompt-Based Engineering for Few-Shot Learners
by: Jaehyung Seo, et al.
Published: (2022-01-01) -
AmericasNLI: Machine translation and natural language inference systems for Indigenous languages of the Americas
by: Katharina Kann, et al.
Published: (2022-12-01) -
A Survey on Evaluation Metrics for Machine Translation
by: Seungjun Lee, et al.
Published: (2023-02-01)