How do you address the challenge of generating repetitive or generic translations with Transformer models?

Generating repetitive or generic translations is a common challenge with Transformer models, and several techniques can be used to address it. One approach is to use techniques that encourage diversity in the generated output, such as temperature sampling or top-k sampling. Temperature sampling involves adjusting the softmax distribution by a temperature parameter, which controls the randomness of the sampling process. A higher temperature makes the distribution more uniform, leading to more diverse outputs. Top-k sampling involves selecting the top k most likely words from the softmax distribution and then sampling from these words. This limits the number of possible words that can be generated, but it also encourages the model to generate more diverse outputs. Another technique to consider is penalty-based decoding. Repetition penalties discourage the model from generating the same words or phrases repeatedly. This is done by penalizing the scores of words that have already been generated, making them less likely to be selected again. Length normalization encourages the model to generate longer and more complete translations. This is done by normalizing the scores of the generated sequences by their length, preventing the model from generating short, generic translations. Data augmentation is another effective technique. By augmenting the training data with examples that are designed to encourage diversity, you can train the model to generate more varied outputs. This can involve techniques such as paraphrasing or back-translation. Beam search with diverse beam search aims to maintain a beam of diverse translation candidates during decoding. Instead of only selecting the top B most likely sequences globally, it enforces diversity amongst the B candidates. This may involve clustering similar translations or adding a diversity penalty to the beam search objective. Another option is fine-tuning the model with specific loss functions designed to promote diversity, such as Maximum Mutual Information (MMI) or similar objectives that encourage the model to generate outputs that are both accurate and different from common outputs. By combining these techniques, it is possible to effectively address the challenge of generating repetitive or generic translations with Transformer models and to generate more diverse, creative, and informative translations.