Can orthogonalizing the embedding matrix make weight tying work better?

1. 1Implement weight tying in a language model by sharing the input embedding and output projection matrices.
2. 2Orthogonalize the embedding matrix using techniques such as Gram-Schmidt orthogonalization or QR decomposition.
3. 3Compare the performance of the model with and without orthogonalization to evaluate the effectiveness of the technique.
4. 4Fine-tune the model with the orthogonalized embedding matrix to optimize its parameters.
5. 5Evaluate the impact of orthogonalization on the model's ability to generalize to new data.

NLP engineers and researchers can benefit from this technique to improve the performance of their language models, and software engineers can apply this to optimize model parameters.

💡 Orthogonalizing the embedding matrix can make the theoretical assumptions behind weight tying more realistic, leading to improved model performance.

✅ Improve weight tying in language models by orthogonalizing the embedding matrix! ✅