Systems And Methods For Training Translation Models Using Source-Augmented Training Examples

A processing system comprising:

• a memory storing a translation model;
• and
• one or more processors coupled to the memory and configured to train the translation model according to a training method comprising:
• for each given training example of a plurality of training examples, the given training example including a first text sequence in a first language, a second text sequence in a second language different from the first language, and a label based on a source of the second text sequence:
• generating, using the translation model, a predicted text sequence based at least in part on the first text sequence and the label of the given training example;
• and
• comparing the predicted text sequence to the second text sequence to generate a loss value for the given training example;
• and
• modifying one or more parameters of the translation model based at least in part on the loss values generated for each of the plurality of training examples.

A processing system comprising:

• a memory storing a translation model;
• and
• one or more processors coupled to the memory and configured to use the translation model to generate a predicted translation of an input text sequence based on the input text sequence and a label,
• wherein the translation model has been trained to generate the predicted translation pursuant to a training method comprising:
• for each given training example of a plurality of training examples, the given training example including a first text sequence in a first language, a second text sequence in a second language different from the first language, and a label based on a source of the second text sequence:
• generating, using the translation model, a predicted text sequence based at least in part on the first text sequence and the label of the given training example;
• and
• comparing the predicted text sequence to the second text sequence to generate a loss value for the given training example;
• and
• modifying one or more parameters of the translation model based at least in part on the loss values generated for each of the plurality of training examples.

A non-transitory computer readable medium comprising instructions which, when executed, cause one or more processors to perform a method comprising:

• training a translation model, wherein the training comprises:
• for each given training example of a plurality of training examples, the given training example including a first text sequence in a first language, a second text sequence in a second language different from the first language, and a label based on a source of the second text sequence:
• generating, using the translation model, a predicted text sequence based at least in part on the first text sequence and the label of the given training example;
• and
• comparing, using the one or more processors, the predicted text sequence to the second text sequence to generate a loss value for the given training example;
• and
• modifying, using the one or more processors, one or more parameters of the translation model based at least in part on the loss values generated for each of the plurality of training examples.