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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In this paper, we investigate the driving factors behind concatenation, a simple but effective data augmentation method for low-resource neural machine translation.
In this paper, we investigate the use of linguistically motivated and computationally efficient structured language models for reranking N-best hypotheses in a statistical machine translation system.
Automatic post-editing (APE) is an important remedy for reducing errors of raw translated texts that are produced by machine translation (MT) systems or software-aided translation.
Non-Autoregressive Neural Machine Translation (NAT) has achieved significant inference speedup by generating all tokens simultaneously.
Recent studies emphasize the need of document context in human evaluation of machine translations, but little research has been done on the impact of user interfaces on annotator productivity and the reliability of assessments.
End-to-end speech translation models have become a new trend in the research due to their potential of reducing error propagation.
Extensive experimental results on various SOD and COD tasks (fully supervised RGB image based SOD, fully supervised RGB-D image pair based SOD, weakly supervised SOD via scribble supervision, and fully supervised RGB image based COD) illustrate that transformer networks can transform salient object detection and camouflaged object detection, leading to new benchmarks for each related task.
We perform neural machine translation of sentence fragments in order to create large amounts of training data for English grammatical error correction.
Neural Machine Translation (NMT) has achieved significant breakthrough in performance but is known to suffer vulnerability to input perturbations.