Translation language modeling is a pre-training objective designed to align token representations across different languages. It involves concatenating sequences from two languages and replacing a certain percentage of tokens with a special mask symbol, such as [MASK]. The model's objective is to maximize the probability of correctly predicting these masked tokens based on the surrounding context. By doing so, the model learns to capture cross-lingual correspondences, as predicting a masked token in one language often requires leveraging information from the unmasked tokens in the other language. This cross-lingual alignment essentially enables the model to function as a translation model.