Machine translation is the task of translating a sentence in a source language to a different target language
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Several mechanisms to focus attention of a neural network on selected parts of its input or memory have been used successfully in deep learning models in recent years.
#28 best model for Machine Translation on WMT2014 English-French
Dictionaries and phrase tables are the basis of modern statistical machine translation systems.
We therefore propose Cross-View Training (CVT), a semi-supervised learning algorithm that improves the representations of a Bi-LSTM sentence encoder using a mix of labeled and unlabeled data.
SOTA for CCG Supertagging on CCGBank
The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration.
Natural language processing tasks, such as question answering, machine translation, reading comprehension, and summarization, are typically approached with supervised learning on taskspecific datasets.
SOTA for Language Modelling on Text8 (using extra training data)
Machine translation systems achieve near human-level performance on some languages, yet their effectiveness strongly relies on the availability of large amounts of parallel sentences, which hinders their applicability to the majority of language pairs.
#2 best model for Machine Translation on WMT2016 English-Russian
Recent works have highlighted the strength of the Transformer architecture on sequence tasks while, at the same time, neural architecture search (NAS) has begun to outperform human-designed models.
#5 best model for Machine Translation on WMT2014 English-German
Feed-forward and convolutional architectures have recently been shown to achieve superior results on some sequence modeling tasks such as machine translation, with the added advantage that they concurrently process all inputs in the sequence, leading to easy parallelization and faster training times.
#11 best model for Machine Translation on WMT2014 English-German