Methods, systems, and apparatus, including computer programs encoded on computer storage media for performing machine learning tasks. One method includes receiving (i) a model input, and (ii) data identifying a first machine learning task to be performed on the model input to generate a first type of model output for the model input; augmenting the model input with an identifier for the first machine learning task to generate an augmented model input; and processing the augmented model input using a machine learning model, wherein the machine learning model has been trained on training data to perform a plurality of machine learning tasks including the first machine learning task, and wherein the machine learning model has been configured through training to process the augmented model input to generate a machine learning model output of the first type for the model input.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for training a machine learning model on training data, wherein the machine learning model is configured to receive an augmented model input comprising a model input with an identifier for a machine learning task to be performed on the model input, and perform the machine learning task on the received augmented model input to generate a respective type of model output for the model input, and wherein the method comprises: obtaining training data comprising a plurality of paired datasets, wherein each of the paired datasets comprise (i) an input dataset, and (ii) an output dataset; and training the machine learning model on the training data to perform a plurality of machine learning tasks, wherein the plurality of machine learning tasks includes the machine learning task to be performed on the model input. 2. The method of claim 1, wherein the augmented model input comprises a model input with a prepended token identifier for the machine learning task. 3. The method of claim 1, wherein the model input comprises a model input of a first input type and the model output comprises a model output of a first output type, and wherein the plurality of paired datasets does not include an input dataset of the first input type paired with an output dataset of the first output type. 4. The method of claim 1, wherein the datasets in the plurality of paired datasets comprise text segments in different languages. 5. The method of claim 4, further comprising generating the training data, comprising: generating vocabularies of a fixed size V in each of the different languages; and merging the generated vocabularies to generate a new vocabulary by sequentially selecting a highest occurring word in each generated vocabulary until a size of the new vocabulary reaches V. 6. The method of claim 4, wherein each paired dataset comprises an input text segment in an input language paired with a text segment in a target language that is different from the input language. 7. The method of claim 6, wherein the plurality of machine learning tasks comprises, for each paired dataset, translating an input text segment into a text segment in the target language. 8. The method of claim 6, wherein the augmented model input comprises a model input with a prepended token indicating at least the target language. 9. A computer-implemented method comprising: receiving (i) a model input, and (ii) data identifying a first machine learning task to be performed on the model input to generate a first type of model output for the model input; augmenting the model input with an identifier for the first machine learning task to generate an augmented model input; and processing the augmented model input using a machine learning model, wherein the machine learning model has been trained on training data to perform a plurality of machine learning tasks including the first machine learning task, and wherein the machine learning model has been configured through training to: process the augmented model input to generate a first machine learning model output of the first type for the model input. 10. The method of claim 9, wherein augmenting the model input with an identifier comprises prepending a token identifier for the first machine learning task to the model input. 11. The method of claim 10, wherein the training data comprises a plurality of paired datasets, wherein each of the paired datasets comprises an input dataset paired with an output dataset, and wherein the model input is of a first type of model input and the plurality of paired datasets does not include a pairing of datasets comprising an input dataset of the first type of model input paired with an output dataset of the first type of model output. 12. The method of claim 11, wherein the datasets in the plurality of paired datasets comprise text segments in different languages, and each paired dataset comprises an input text segment in an input language paired with a text segment in a target language that is different from the input language. 13. The method of claim 12, wherein the plurality of machine learning tasks comprises, for each paired dataset, translating an input text segment into a text segment in the target language. 14. The method of claim 12, wherein augmenting the model input with an identifier for the machine learning task to generate an augmented model input comprises prepending a token indicating at least the target language to the model input. 15. A system comprising one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising: receiving (i) a model input, and (ii) data identifying a first machine learning task to be performed on the model input to generate a first type of model output for the model input; augmenting the model input with an identifier for the first machine learning task to generate an augmented model input; and processing the augmented model input using a machine learning model, wherein the machine learning model has been trained on training data to perform a plurality of machine learning tasks including the first machine learning task, and wherein the machine learning model has been configured through training to: process the augmented model input to generate a machine learning model output of the first type for the model input. 16. The system of claim 15, wherein the machine learning model comprises (i) an encoder subsystem configured to receive an augmented model input, and (ii) a decoder subsystem configured to generate a model output. 17. The system of claim 16, wherein the encoder subsystem and decoder subsystem comprise respective recurrent neural networks. 18. The system of claim 17, wherein the decoder subsystem comprises an attention mechanism. 19. The system of claim 15, wherein the augmented model input comprises a model input with a prepended token identifier for the machine learning task. 20. The system of claim 15, wherein the plurality of machine learning tasks comprises translating an input text segment into a text segment in a target language.