A sequence to sequence (Seq2Seq) model which translates English sentences into their corresponding French sentences.
Neural Machine Translation, also referred to as Neural MT, NMT, Deep Neural Machine Translation, Deep NMT, or DNMT, is a cutting-edge machine translation method that uses neural network techniques to estimate the likelihood of a sequence of words. This might be a word or sentence in its entirety, or thanks to recent developments, the entire document. Deep neural networks and artificial intelligence are used in NMT to train neural models, which is a fundamentally different approach to the problem of language translation and localization. In just three years, there has been a significant shift from SMT to NMT, making NMT the main machine translation methodology. With superior fluency and adequacy than statistical machine translation methods, neural machine translation often delivers translations of a significantly higher quality.
Only a small portion of the memory required by conventional Statistical Machine Translation (SMT) models is used by neural machine translation. Because the neural translation model is trained end-to-end to maximise translation performance, this NMT approach differs from traditional translation SMT systems. In contrast to the conventional phrase-based translation system, which consists of numerous small sub-components that are tuned separately, neural machine translation aims to create and train a single, massive neural network that can read a sentence and produce an accurate translation.
Link: https://www.kaggle.com/datasets/devicharith/language-translation-englishfrench
The dataset comprises 2 columns, one of which includes English words/sentences and the other contains the corresponding French words/sentences i.e. French translations.
- Keras
- Tensorflow
- Scikit-learn
- Numpy
- Pandas
- Seaborn
- Matplotlib
For getting more datasets of distinct languages, refer to the following link: http://www.manythings.org/anki/