Neural Network-based Language Model (LM) Training Algorithm
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A Neural Network-based Language Model (LM) Training Algorithm is a neural network training algorithm that can be implemented by a Neural LM Training System (to solve a [[Neural Network Language Model_Training_Task]] to produce a neural network language model).
- AKA: Neural Network Language Model Training_Algorithm.
- Context:
- It can typically enhance the performance of Neural Network-based Language Models with training data.
- It can often optimize the learning process through gradient descent mechanisms.
- It can range from being a Simple Neural Network Algorithm to being a Complex Neural Network Algorithm, depending on its architecture.
- It can integrate with External Training Frameworks for improved training efficacy.
- ...
- Examples:
- Recurrent Neural Network-based Language_Model_Algorithm, such as an [[Long Short-Term Memory-based Language_Model_Algorithm]].
- a [[Convolutional Neural Network-based Language_Model_Algorithm]].
- Skip-Gram Neural Network Language Model Algorithm.
- Continuous Bag of Words Neural Network Language Model_Algorithm.
- ...
- Counter-Examples:
- n-Gram Backoff Language Model, which lacks neural network components.
- an Maximum Likelihood Estimation-based Language_Model Algorithm, which uses traditional statistical methods.
- See: Matrix Factorization-based Language Model, Distributional Continuous Dense Word Model Training Algorithm.