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 ...

To generate each part of translation, the attention mechanism tells a Neural Machine Translation model where it should pay attention to. A simple encoder-decoder model without the attention mechanism ...

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

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 ...

First, let us understand why an Attention Mechanism made machine translation easy. Previously encoder-decoder models were used for machine translation. The encoder-decoder model contains two networks ...

Recent research sheds light on the strengths and weaknesses of encoder-decoder and decoder-only models architectures in machine translation tasks.

What is Attention? Attention is simply a vector, often the outputs of dense layer using softmax function. Before Attention mechanism, translation relies on reading a complete sentence and compress all ...

Google is offering free AI courses that can help professionals and students to upskill themselves. From introduction into LLMs to generative AI, these courses equip learners with skills needed to ...

Recently many studies in neural machine translation have attempted to obtain high-quality multimodal representation of encoder or decoder via attention mechanism. However, attention mechanism does not ...