Neural Network Language Model

Neural network language models (NNLMs) are computational models that learn to predict the probability of sequences of words, aiming to capture the statistical regularities of human language. Current research focuses on improving NNLMs' accuracy and efficiency through techniques like adaptive multi-corpora training, mixed-precision quantization, and novel training objectives that better align with evaluation metrics, as well as exploring architectures such as transformers and incorporating finite state transducers for improved performance and resource efficiency. These advancements have significant implications for applications like speech recognition, virtual assistants, and understanding cognitive processes, particularly in areas like dementia research where NNLMs are being used to model linguistic anomalies.