Method and system of utilizing unsupervised learning to improve text to content suggestions
AssigneeMicrosoft Technology Licensing, LLC
InventorsXingxing Zhang, Ji Li, Furu Wei, Ming Zhou, Amit Srivastava
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Abstract
A method and system for providing an application-specific embedding for an entire text-to-content suggestions service is disclosed. The method includes accessing a dataset containing unlabeled training data collected from an application, the unlabeled training data being collected under user privacy constraints, applying an unsupervised ML model to the dataset to generate a pretrained embedding; and utilizing the pretrained embedding to train the text-to-content suggestion ML model utilized by the application.
Claims20
A data processing system comprising:
- a processor;
- and
- a memory in communication with the processor, the memory comprising executable instructions that, when executed by the processor, cause the data processing system to perform functions of:
- accessing a dataset containing unlabeled training data collected from users' use of an application that offers text-to-content suggestions, text-to-content suggestions including at least one of text-to-image suggestions, text-to-icon suggestions and text-to-emoticon suggestions, the unlabeled training data including an unordered set of words to ensure privacy;
- and
- applying an unsupervised training process to the dataset to generate a domain-specific pretrained embedding for the application;
- wherein the pretrained embedding is configured to be used to train a text-to-content suggestion ML model utilized by the application to suggest content in response to a text query.
A method for training a text-to-content suggestion machine-learning (ML) model, the method comprising:
- accessing a dataset containing unlabeled training data collected from users' use of an application that offers text-to-content suggestions, text-to-content suggestions including at least one of text-to-image suggestions, text-to-icon suggestions and text-to-emoticon suggestions, the unlabeled training data including an unordered set of words to ensure privacy;
- and
- applying an unsupervised training process to the dataset to generate a domain-specific pretrained embedding for the application;
- wherein the pretrained embedding is configured to be used to train a text-to-content suggestion ML model utilized by the application to suggest content in response to a text query.
A non-transitory computer readable medium on which are stored instructions that, when executed, cause a programmable device to:
- access a dataset containing unlabeled training data collected from users' use of an application that offers text-to-content suggestions, text-to-content suggestions including at least one of text-to-image suggestions, text-to-icon suggestions and text-to-emoticon suggestions, the unlabeled training data including an unordered set of words to ensure privacy;
- and
- apply an unsupervised training process to the dataset to generate a domain-specific pretrained embedding for the application;
- wherein the pretrained embedding is configured to be used to train a text-to-content suggestion ML model utilized by the application to suggest content in response to a text query.
Dependencies are inferred from the claim text.