Monday, August 22, 2022

New Tutorial: NMT with Bahdanau's Attention Using TensorFlow and Keras

Hi,

This week you'll learn about Neural Machine Translation with Bahdanau's Attention Using TensorFlow and Keras.

It is the year 3000. You are a scientist at NASA, stationed on Mars. Space travel is very much possible, with humanity able to traverse galaxies at a time. You look at your very own Galaxy Traveler x270 spacecraft. Everybody has one of these nowadays. Just the other day, your 15-year-old niece came to meet you from two galaxies away.

These grand peaks of development reached by humanity make you think how important the first step was. The reason why you can travel through space is that humanity figured out the principles of rocket science. It may have started with a small model rocket built in a backyard, but scaling up the same science took us to the moon.

Now, you might wonder what rockets have to do with our topic today: Neural Machine Translation. You see, if we compare the perspectives, Neural Machine Translation, or Attention, is our backyard model rocket. The futuristic galaxy-traveling spaceship becomes Transformer language models.

To understand the beauty of Transformers, it is essential to understand the foundational blocks of the concept, that is, Attention.

Today's blog studies Neural Machine Translation with Bahdanau's Attention.

The big picture: Following up on last week's tutorial, we have the first approach to attention under the microscope: Bahdanau's Attention. It is a content-based attention mechanism that attends to the most relevant parts of the input sequence based on the current decoder state.

How it works: Also known as additive attention, we calculate the dependencies and alignment of input to the output. Bahdanau's Attention uses a single-layer feedforward network to calculate the alignment scores.

Our thoughts: Bahdanau's Attention is great at building dependencies between input and output, consequently giving us high accuracy.

Yes, but: High accuracy doesn't necessarily translate to higher efficiency. Hence, Bahdanau's Attention isn't flawless, despite its beautiful results.

Stay smart: Look out for our next tutorial in this series, encompassing another variant of attention, known as Luong's Attention. Both these methods are great, and it's important to understand where exactly they differ.

Click here to read the full tutorial

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Note: You may have missed this, but last Wednesday, we published a new post on Multi-Task Learning and HydraNets with PyTorch.