Using the same weights for both the embedding layer and the layer just before the softmax (output) layer in NLP models serves a specific purpose known as tying the word embeddings and output layer weights. This technique has several advantages:
Parameter Efficiency: By tying the embedding weights and output layer weights, you significantly reduce the number of parameters in the model. In traditional softmax-based language models, the number of output layer parameters is proportional to the vocabulary size, which can be enormous in large-scale applications. Tying the weights reduces the model's memory and computational requirements.
Regularization: Tied embeddings and output weights act as a form of regularization. It encourages the model to learn a more compact and coherent representation of words. This regularization can help prevent overfitting, especially in data-constrained scenarios.
Improved Generalization: Tied embeddings and output weights encourage the model to learn a better alignment between word representations in the embedding space and their distributional properties in the output space. This improved alignment can lead to better generalization and model performance.
Semantic Consistency: Tying the weights can enforce a semantic consistency between the embedding space and the output space. It encourages the model to capture semantic relationships between words more effectively, leading to better word representations.
Reduced Risk of Overfitting: With a smaller number of parameters, the model is less prone to overfitting, which is particularly important when dealing with limited training data.
Transfer Learning: Models with tied weights can serve as better starting points for transfer learning. Pre-training on a large corpus with tied weights and fine-tuning on a specific downstream task often leads to improved performance compared to models without weight tying.
Computation Efficiency: Smaller models with fewer parameters are computationally more efficient during training and inference, making them suitable for resource-constrained environments.
Common examples of models that employ weight tying include:
- Word2Vec: In Word2Vec models, the input word embeddings (word vectors) are tied to the output weights, specifically in the Skip-gram model.
- FastText: FastText embeddings, which are based on subword information, often employ weight tying when used in text classification or other tasks.
- ELMo (Embeddings from Language Models): ELMo's output layer weights are tied to the input word embeddings.
Overall, tying the word embeddings and output layer weights is a technique that balances model efficiency, regularization, and improved generalization, making it a valuable strategy in various NLP applications.