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tags: |
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- seq2seq |
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- character-level |
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- machine translation |
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--- |
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## Tensorflow Keras Implementation of Character-level Recurrent Sequence-to-Sequence Model |
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This repo contains code using the model. [Character-level recurrent sequence-to-sequence model](https://keras.io/examples/nlp/lstm_seq2seq/). |
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Credits: [fchollet](https://twitter.com/fchollet) - Original Author |
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HF Contribution: [Rishav Chandra Varma](https://huggingface.co/reichenbach) |
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## Background Information |
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### Introduction |
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This example demonstrates how to implement a basic character-level recurrent sequence-to-sequence model. We apply it to translating short English sentences into short French sentences, character-by-character. Note that it is fairly unusual to do character-level machine translation, as word-level models are more common in this domain. |
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### Summary of the algorithm |
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* We start with input sequences from a domain (e.g. English sentences) and corresponding target sequences from another domain (e.g. French sentences). |
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* An encoder LSTM turns input sequences to 2 state vectors (we keep the last LSTM state and discard the outputs). |
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* A decoder LSTM is trained to turn the target sequences into the same sequence but offset by one timestep in the future, a training process called "teacher forcing" in this context. It uses as initial state the state vectors from the encoder. Effectively, the decoder learns to generate targets[t+1...] given targets[...t], conditioned on the input sequence. |
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* In inference mode, when we want to decode unknown input sequences, we: - Encode the input sequence into state vectors - Start with a target sequence of size 1 (just the start-of-sequence character) - Feed the state vectors and 1-char target sequence to the decoder to produce predictions for the next character - Sample the next character using these predictions (we simply use argmax). - Append the sampled character to the target sequence - Repeat until we generate the end-of-sequence character or we hit the character limit. |
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