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Found 12 papers, 11 papers with code
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.
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.
How Much Attention Do You Need? A Granular Analysis of Neural Machine Translation Architectures
With recent advances in network architectures for Neural Machine Translation (NMT) recurrent models have effectively been replaced by either convolutional or self-attentional approaches, such as in the Transformer.
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.
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.
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.
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.
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.
A Shocking Amount of the Web is Machine Translated: Insights from Multi-Way Parallelism
We show that content on the web is often translated into many languages, and the low quality of these multi-way translations indicates they were likely created using Machine Translation (MT).
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.
Trained MT Metrics Learn to Cope with Machine-translated References
Neural metrics trained on human evaluations of MT tend to correlate well with human judgments, but their behavior is not fully understood.
