Survey of Low-Resource Machine Translation
We present a survey covering the state of the art in low-resource machine translation (MT) research. There are currently around 7,000 languages spoken in the world and almost all language pairs lack significant resources for training machine translation models. There has been increasing interest in...
Main Authors: | Barry Haddow, Rachel Bawden, Antonio Valerio Miceli Barone, Jindřich Helcl, Alexandra Birch |
---|---|
Format: | Article |
Language: | English |
Published: |
The MIT Press
2022-06-01
|
Series: | Computational Linguistics |
Online Access: | http://dx.doi.org/10.1162/coli_a_00446 |
Similar Items
-
Decoding Strategies for Improving Low-Resource Machine Translation
by: Chanjun Park, et al.
Published: (2020-09-01) -
Keeping Models Consistent between Pretraining and Translation for Low-Resource Neural Machine Translation
by: Wenbo Zhang, et al.
Published: (2020-11-01) -
The Task of Post-Editing Machine Translation for the Low-Resource Language
by: Diana Rakhimova, et al.
Published: (2024-01-01) -
Boosting the Transformer with the BERT Supervision in Low-Resource Machine Translation
by: Rong Yan, et al.
Published: (2022-07-01) -
Part-of-Speech Tags Guide Low-Resource Machine Translation
by: Zaokere Kadeer, et al.
Published: (2023-08-01)