In this exercise, I built an English-to-Portuguese neural machine translation (NMT) model using LSTM networks with attention, based on the starting code, instructions, and utility functions from the ...

Previously encoder-decoder models were used for machine translation. The encoder-decoder model contains two networks encoder and decoder. The encoder model encodes the input sequence into a vector.

I implement encoder-decoder based seq2seq models with attention using Keras. The encoder can be a Bidirectional LSTM, a simple LSTM, or a GRU, and the decoder can be an LSTM or a GRU. I evaluate the ...

Neural machine translation by jointly learning to align and translate. arXiv:1409.0473 (2014). [3] Cho, Kyunghyun, Aaron Courville, and Yoshua Bengio. Describing Multimedia Content using ...

The main purpose of multimodal machine translation is to improve the quality of translation results by taking the corresponding visual context as an additional input. Recently many studies in neural ...

Attention mechanisms have been successfully applied to various sequence-to-sequence learning tasks, such as machine translation, speech recognition, text summarization, image captioning, or ...

Google is offering free AI courses that can help professionals and students to upskill themselves. From introduction into ...

Large language models (LLMs) have changed the game for machine translation (MT). LLMs vary in architecture, ranging from decoder-only designs to encoder-decoder frameworks. Encoder-decoder models, ...

The main purpose of multimodal machine translation (MMT) is to improve the quality of translation results by taking the corresponding visual context as an additional input. Recently many studies in ...