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 experiment using speaker embeddings learned along with the model training, as well as one-hot vectors and x-vectors.
Recent progress in the task of Grammatical Error Correction (GEC) has been driven by addressing data sparsity, both through new methods for generating large and noisy pretraining data and through the publication of small and higher-quality finetuning data in the BEA-2019 shared task.
We release a multilingual neural machine translation model, which can be used to translate text in the biomedical domain.
It introduces under-resourced languages in terms of machine translation and how orthographic information can be utilised to improve machine translation.
Recent work demonstrates the potential of multilingual pretraining of creating one model that can be used for various tasks in different languages.
Recent studies have demonstrated the overwhelming advantage of cross-lingual pre-trained models (PTMs), such as multilingual BERT and XLM, on cross-lingual NLP tasks.
The technique does not require any knowledge of the structure or weights of the target DNN.
The preparation of raw parallel corpus, sentiment analysis of the sentences and the training of a Character Based Neural Machine Translation model using the same has been discussed extensively in this paper.
The global pandemic of COVID-19 has made the public pay close attention to related news, covering various domains, such as sanitation, treatment, and effects on education.
Transformer has been widely-used in many Natural Language Processing (NLP) tasks and the scaled dot-product attention between tokens is a core module of Transformer.