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t. e. Google Neural Machine Translation (GNMT) was a neural machine translation (NMT) system developed by Google and introduced in November 2016 that used an artificial neural network to increase fluency and accuracy in Google Translate. [1][2][3][4] The neural network consisted of two main blocks, an encoder and a decoder, both of LSTM ...
Neural machine translation. Neural machine translation (NMT) is an approach to machine translation that uses an artificial neural network to predict the likelihood of a sequence of words, typically modeling entire sentences in a single integrated model. It is the dominant approach today [1]: 293 [2]: 1 and can produce translations that rival ...
NiuTrans.SMT is an open-source statistical machine translation system jointly developed by the Natural Language Processing Laboratory of Northeastern University and Shenyang Yayi Network Technology Co., Ltd. NiuTrans.NMT is a lightweight and efficient Transformer-based neural machine translation system. It is implemented with pure C++ and it is ...
BERT (language model) Bidirectional encoder representations from transformers (BERT) is a language model introduced in October 2018 by researchers at Google. [1][2] It learned by self-supervised learning to represent text as a sequence of vectors. It had the transformer encoder architecture. It was notable for its dramatic improvement over ...
A large language model (LLM) is a computational model capable of language generation or other natural language processing tasks. As language models, LLMs acquire these abilities by learning statistical relationships from vast amounts of text during a self-supervised and semi-supervised training process. [ 1 ]
e. Generative Pre-trained Transformer 2 (GPT-2) is a large language model by OpenAI and the second in their foundational series of GPT models. GPT-2 was pre-trained on a dataset of 8 million web pages. [2] It was partially released in February 2019, followed by full release of the 1.5-billion-parameter model on November 5, 2019. [3][4][5]
Long short-term memory (LSTM) [1] is a type of recurrent neural network (RNN) aimed at dealing with the vanishing gradient problem [2] present in traditional RNNs. Its relative insensitivity to gap length is its advantage over other RNNs, hidden Markov models and other sequence learning methods. It aims to provide a short-term memory for RNN ...
Machine learningand data mining. An autoencoder is a type of artificial neural network used to learn efficient codings of unlabeled data (unsupervised learning). [1][2] An autoencoder learns two functions: an encoding function that transforms the input data, and a decoding function that recreates the input data from the encoded representation.