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Google’s Neural Machine Translation Paper

Google’s Neural Machine Translation Paper


  1. Left Encoder Network: The left encoder network typically consists of a series of layers that process the input sentence in the source language. These layers could include Convolutional Neural Networks (CNNs) or Recurrent Neural Networks (RNNs). GPUs (Graphics Processing Units) are used to accelerate the computation of these layers. The input to the left encoder network is the source sentence in a tokenized or word-embedded form, and the output is a hidden representation of this source sentence.
    • Input: Tokenized or word-embedded source sentence.
    • Output: Hidden representation (context) of the source sentence.
  2. Encoder LSTM Layer: An LSTM (Long Short-Term Memory) layer is a type of recurrent neural network layer used within the left encoder network. It processes the tokenized or word-embedded source sentence sequentially and captures the dependencies between words in the source language. The LSTM layer outputs a sequence of hidden states that represent the source sentence context.
  3. Middle Attention Module: The attention module is a crucial part of NMT architectures. It helps the model focus on specific parts of the source sentence when generating the target sentence. The attention mechanism calculates a set of attention scores, indicating the importance of different parts of the source sentence at each decoding step.
  4. Right Decoder Network: The right decoder network generates the target sentence in the desired language. Similar to the left encoder network, it may consist of LSTM layers or other recurrent layers. It takes the hidden representation of the source sentence (context) and the previously generated target words as input. It produces the probability distribution over the target vocabulary for generating the next word.
    • Input: Hidden representation of the source sentence and previously generated target words.
    • Output: Probability distribution over the target vocabulary.
  5. Softmax with Example: Softmax is a mathematical function used to convert a vector of raw scores (logits) into a probability distribution over a set of possible outcomes (in this case, target words). It takes the exponential of each raw score and normalizes them to sum to 1.0. This helps in selecting the most likely word for the next position in the target sentence.

    Example: Let’s say we have raw scores for three words: [3.0, 1.0, 0.2]. Applying softmax, we get:

    softmax([3.0, 1.0, 0.2]) = [0.836, 0.113, 0.051]

    This means that the model predicts the first word with a high probability (83.6%), the second word with a moderate probability (11.3%), and the third word with a low probability (5.1%).

  6. Decoder LSTM Layer: The decoder LSTM layer is responsible for generating the target sentence one word at a time. It takes the hidden representation of the source sentence (context) and the previously generated target words as input and produces the next word in the target sentence. It does this by updating its internal state based on the context and previously generated words.

These components work together in a neural machine translation system to take a source sentence in one language and generate a target sentence in another language. The attention mechanism and decoder LSTM layer are crucial for ensuring that the model generates coherent and contextually accurate translations. Please refer to the specific paper you mentioned for the exact details and numbers related to the Google NMT system.

Google’s Neural Machine Translation Paper





Google’s neural machine translation

Read the following article:

Wu, Yonghui, et al. (2016). Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation.

Then, describe the model architecture presented in figure 1 with some real numbers:

  1. left encoder network
    1. what are the GPUs layers about: explain inputs/outputs
    2. what is Encoder LSTM layer about
  2. middle attention module
  3. right decoder network
    1. explain Softmax with example
    2. what is Decoder LSTM layer about

You will need the following articles to help you with descriptions:

See rubric for grading.

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