A study of residual adapters for multi-domain neural machine translation
Domain adaptation is an old and vexing problem for machine translation systems. The most
common and successful approach to supervised adaptation is to fine-tune a baseline system …
common and successful approach to supervised adaptation is to fine-tune a baseline system …
A Study of Residual Adapters for Multi-Domain Neural Machine Translation
MQ Pham, JM Crego, F Yvon… - Conference …, 2020 - universite-paris-saclay.hal.science
Domain adaptation is an old and vexing problem for machine translation systems. The most
common and successful approach to supervised adaptation is to fine-tune a baseline system …
common and successful approach to supervised adaptation is to fine-tune a baseline system …
A Study of Residual Adapters for Multi-Domain Neural Machine Translation
MQ Pham, JM Crego, F Yvon… - Conference on Machine …, 2020 - hal.umontpellier.fr
Domain adaptation is an old and vexing problem for machine translation systems. The most
common and successful approach to supervised adaptation is to fine-tune a baseline system …
common and successful approach to supervised adaptation is to fine-tune a baseline system …
A Study of Residual Adapters for Multi-Domain Neural Machine Translation
MQ Pham, JM Crego, F Yvon… - Proceedings of the Fifth …, 2020 - aclanthology.org
Abstract Domain adaptation is an old and vexing problem for machine translation systems.
The most common approach and successful to supervised adaptation is to fine-tune a …
The most common approach and successful to supervised adaptation is to fine-tune a …
A Study of Residual Adapters for Multi-Domain Neural Machine Translation
MQ Pham, JM Crego, F Yvon… - Conference on Machine …, 2020 - inria.hal.science
Domain adaptation is an old and vexing problem for machine translation systems. The most
common and successful approach to supervised adaptation is to fine-tune a baseline system …
common and successful approach to supervised adaptation is to fine-tune a baseline system …
[PDF][PDF] A Study of Residual Adapters for Multi-Domain Neural Machine Translation
MQ Pham, J Crego, F Yvon, J Senellart - statmt.org
Abstract Domain adaptation is an old and vexing problem for machine translation systems.
The most common and successful approach to supervised adaptation is to fine-tune a …
The most common and successful approach to supervised adaptation is to fine-tune a …
[PDF][PDF] A Study of Residual Adapters for Multi-Domain Neural Machine Translation
MQ Pham, J Crego, F Yvon, J Senellart - statmt.org
Abstract Domain adaptation is an old and vexing problem for machine translation systems.
The most common and successful approach to supervised adaptation is to fine-tune a …
The most common and successful approach to supervised adaptation is to fine-tune a …
A Study of Residual Adapters for Multi-Domain Neural Machine Translation
MQ Pham, JM Crego, F Yvon… - Conference on Machine …, 2020 - hal.univ-smb.fr
Domain adaptation is an old and vexing problem for machine translation systems. The most
common and successful approach to supervised adaptation is to fine-tune a baseline system …
common and successful approach to supervised adaptation is to fine-tune a baseline system …
[PDF][PDF] A Study of Residual Adapters for Multi-Domain Neural Machine Translation
MQ Pham, JM Crego, F Yvon, J Senellart - hal.science
Abstract Domain adaptation is an old and vexing problem for machine translation systems.
The most common and successful approach to supervised adaptation is to fine-tune a …
The most common and successful approach to supervised adaptation is to fine-tune a …
[PDF][PDF] A Study of Residual Adapters for Multi-Domain Neural Machine Translation
MQ Pham, J Crego, F Yvon, J Senellart - academia.edu
Abstract Domain adaptation is an old and vexing problem for machine translation systems.
The most common and successful approach to supervised adaptation is to fine-tune a …
The most common and successful approach to supervised adaptation is to fine-tune a …