Search Results for author:
Found 15 papers, 8 papers with code
Improving the Quality Trade-Off for Neural Machine Translation Multi-Domain Adaptation
Building neural machine translation systems to perform well on a specific target domain is a well-studied problem.
Sockeye 2: A Toolkit for Neural Machine Translation
We present Sockeye 2, a modernized and streamlined version of the Sockeye neural machine translation (NMT) toolkit.
Analyzing the Use of Influence Functions for Instance-Specific Data Filtering in Neural Machine Translation
Customer feedback can be an important signal for improving commercial machine translation systems.
Sockeye 3: Fast Neural Machine Translation with PyTorch
When running comparable models, Sockeye 3 is up to 126% faster than other PyTorch implementations on GPUs and up to 292% faster on CPUs.
The Devil is in the Details: On the Pitfalls of Vocabulary Selection in Neural Machine Translation
Vocabulary selection, or lexical shortlisting, is a well-known technique to improve latency of Neural Machine Translation models by constraining the set of allowed output words during inference.
The Sockeye 2 Neural Machine Translation Toolkit at AMTA 2020
We present Sockeye 2, a modernized and streamlined version of the Sockeye neural machine translation (NMT) toolkit.
Image Captioning as Neural Machine Translation Task in SOCKEYE
Image captioning is an interdisciplinary research problem that stands between computer vision and natural language processing.
Sockeye: A Toolkit for Neural Machine Translation
Written in Python and built on MXNet, the toolkit offers scalable training and inference for the three most prominent encoder-decoder architectures: attentional recurrent neural networks, self-attentional transformers, and fully convolutional networks.
Using Target-side Monolingual Data for Neural Machine Translation through Multi-task Learning
The performance of Neural Machine Translation (NMT) models relies heavily on the availability of sufficient amounts of parallel data, and an efficient and effective way of leveraging the vastly available amounts of monolingual data has yet to be found.
