Neural Network-based Language Model (LM) Training System

A Neural Network-based Language Model (LM) Training System is an LM training system that implements a neural LM training algorithm.

• Context:
  • It can range from being a Neural Word-level LM System to being a Neural Character-level LM System.
• Example(s):
  • an RNN-based LM System, such as an LSTM-based LM system.
  • a Convolutional NNet-based LM System.
  • a Python-based Neural LM System, such as a Pytorch-based LM Training System.
• Counter-Example(s):
  • a Maximum Likelihood-based LM System.
• See: Neural Language Generation System.

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References

2018

• https://github.com/PetrochukM/PyTorch-NLP/tree/master/examples/awd-lstm-lm
  • QUOTE: awd-lstm-lm set the state-of-the-art in word level perplexities in 2017. With PyTorch NLP, we show that in 30 minutes, we were able to reduce the footprint of this repository by 4 files (185 lines of code). We employ the use of the datasets package, IdentityEncoder module, BPTTBatchSampler module, LockedDropout module and WeightDrop module

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