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Found 16 papers, 6 papers with code
Surprise Language Challenge: Developing a Neural Machine Translation System between Pashto and English in Two Months
We find that starting from an existing large model pre-trained on 50languages leads to far better BLEU scores than pretraining on one high-resource language pair with a smaller model.
The University of Edinburgh’s English-German and English-Hausa Submissions to the WMT21 News Translation Task
This paper presents the University of Edinburgh’s constrained submissions of English-German and English-Hausa systems to the WMT 2021 shared task on news translation.
The University of Edinburgh’s English-Tamil and English-Inuktitut Submissions to the WMT20 News Translation Task
We describe the University of Edinburgh’s submissions to the WMT20 news translation shared task for the low resource language pair English-Tamil and the mid-resource language pair English-Inuktitut.
Survey of Low-Resource Machine Translation
We present a survey covering the state of the art in low-resource machine translation research.
The University of Edinburgh's Submissions to the WMT19 News Translation Task
For all translation directions, we created or used back-translations of monolingual data in the target language as additional synthetic training data.
The University of Edinburgh's Submissions to the WMT18 News Translation Task
The University of Edinburgh made submissions to all 14 language pairs in the news translation task, with strong performances in most pairs.
The University of Edinburgh's Neural MT Systems for WMT17
This paper describes the University of Edinburgh's submissions to the WMT17 shared news translation and biomedical translation tasks.
Regularization techniques for fine-tuning in neural machine translation
We investigate techniques for supervised domain adaptation for neural machine translation where an existing model trained on a large out-of-domain dataset is adapted to a small in-domain dataset.
Deep Architectures for Neural Machine Translation
It has been shown that increasing model depth improves the quality of neural machine translation.
A parallel corpus of Python functions and documentation strings for automated code documentation and code generation
Automated documentation of programming source code and automated code generation from natural language are challenging tasks of both practical and scientific interest.
Nematus: a Toolkit for Neural Machine Translation
We present Nematus, a toolkit for Neural Machine Translation.
Towards cross-lingual distributed representations without parallel text trained with adversarial autoencoders
Current approaches to learning vector representations of text that are compatible between different languages usually require some amount of parallel text, aligned at word, sentence or at least document level.
Low-rank passthrough neural networks
Various common deep learning architectures, such as LSTMs, GRUs, Resnets and Highway Networks, employ state passthrough connections that support training with high feed-forward depth or recurrence over many time steps.
