Unsmoothed Maximum-Likelihood Character-level Language Modeling System

An Unsmoothed Maximum-Likelihood Character-level Language Modeling System is a maximum-likelihood character-level language modeling system that is a unsmoothed maximum-likelihood language modeling system and that implements an Unsmoothed Maximum-Likelihood Character-level Language Modeling Algorithm to solve an Unsmoothed Maximum-Likelihood Character-level Language Modeling Task.

• Context:
  • …
• Example(s):
  • as shown in (Goldberg, 2015).
  • …
• Counter-Example(s):
  • a RNN-based Character-level Language Modeling Algorithm.
  • a Smoothed Maximum-Likelihood Character-level Language Modeling Algorithm.
  • an Unsmoothed Maximum-Likelihood Word-level Language Modeling Algorithm.
• See: Python-based Character-level Language Modeling System.

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References

2015

• (Goldberg, 2015) ⇒ Yoav Goldberg. (2015). “The Unreasonable Effectiveness of Character-level Language Models (and Why RNNs Are Still Cool).” In: Blog Post.
  • QUOTE: However, it feels to me that most readers of the post are impressed by the wrong reasons. This is because they are not familiar with unsmoothed maximum-likelihood character level language models and their unreasonable effectiveness at generating rather convincing natural language outputs. ...

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