Neural Probabilistic Language Model (2003) › Limitations and Challenges

Despite its groundbreaking contributions, the Neural Probabilistic Language Model faced several limitations that subsequent research would address. The model's computational complexity was significant, especially when dealing with large vocabularies. The softmax layer over the entire vocabulary required computing probabilities for every word, making training and inference expensive. The context window was fixed and relatively short, limiting the model's ability to capture long-range dependencies in text. Additionally, the model required careful initialization and tuning of hyperparameters, and training could be slow on the computational hardware available in 2003.

The model also struggled with out-of-vocabulary words, as it still operated on a fixed vocabulary. Rare words or newly coined terms that didn't appear in the training data would be completely unknown to the model. This limitation would later be addressed by subword tokenization methods like Byte Pair Encoding (BPE) and SentencePiece, which break words into smaller components that can be learned and recombined.