Machine translation is the task of translating a sentence in a source language to a different target language
( Image credit: Google seq2seq )
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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.
Ranked #1 on CCG Supertagging on CCGBank
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.
Ranked #37 on Machine Translation on WMT2014 English-French
Dictionaries and phrase tables are the basis of modern statistical machine translation systems.
The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration.
Ranked #1 on Machine Translation on IWSLT2015 English-German
This paper demonstrates that multilingual denoising pre-training produces significant performance gains across a wide variety of machine translation (MT) tasks.
We evaluate a number of noising approaches, finding the best performance by both randomly shuffling the order of the original sentences and using a novel in-filling scheme, where spans of text are replaced with a single mask token.
Ranked #2 on Text Summarization on X-Sum
Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP).
Ranked #1 on Natural Language Inference on MultiNLI
ABSTRACTIVE TEXT SUMMARIZATION COMMON SENSE REASONING COREFERENCE RESOLUTION DOCUMENT SUMMARIZATION LINGUISTIC ACCEPTABILITY MACHINE TRANSLATION NATURAL LANGUAGE INFERENCE QUESTION ANSWERING SEMANTIC TEXTUAL SIMILARITY SENTIMENT ANALYSIS TEXT CLASSIFICATION TRANSFER LEARNING WORD SENSE DISAMBIGUATION
Unsupervised pre-training of large neural models has recently revolutionized Natural Language Processing.