Neural-based Character-Level Language Model (LM) Training Algorithm

A Neural-based Character-Level Language Model (LM) Training Algorithm is a character-level language modeling algorithm that is a Neural-based LM Algorithm.

• Context:
  • It can be implemented by a Neural-based Character-Level Language Modeling System.
• Example(s):
  • an LSTM-based Character-Level LM Algorithm, such as in (Karpathy, 2015).
  • …
• Counter-Example(s):
  • an MaxLikelihood-based Character-Level LM Algorithm.
  • a Neural-based Word-Level Language Modeling Algorithm.
• See: Language Modeling.

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References

2018

• https://github.com/PetrochukM/PyTorch-NLP/tree/master/examples/awd-lstm-lm
  • QUOTE: This repository contains the code used for two Salesforce Research papers:
    • Regularizing and Optimizing LSTM Language Models.
    • An Analysis of Neural Language Modeling at Multiple Scales
      This code was originally forked from the PyTorch word level language modeling example.

2015

• (Karpathy, 2015) ⇒ Andrej Karpathy. (2015). “The Unreasonable Effectiveness of Recurrent Neural Networks.” In: Proceedings of Blog post 2015-05-21.
  • QUOTE: ... By the way, together with this post I am also releasing code on Github that allows you to train character-level language models based on multi-layer LSTMs. You give it a large chunk of text and it will learn to generate text like it one character at a time. ...

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