Dynamic Language Models for Continuously Evolving Content
AssigneeGoogle Inc.
InventorsSpurthi Amba Hombaiah, Mingyang Zhang, Michael Bendersky, Tao Chen, Marc Alexander Najork
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Abstract
Provided are systems and methods for incremental training of machine learning models to adapt to changes in an underlying data distribution. One example setting in which the techniques described herein may be beneficial is for incrementally training natural language models to enable the models to have or adapt to a dynamically changing vocabulary. Incremental training is provided as a feasible and inexpensive way of adapting machine learning models to evolving vocabulary without having to retrain them from scratch.
Claims22
A computer-implemented method for performing machine learning, the method comprising:
- obtaining, by a computing system comprising one or more computing devices, a first version of a machine-learned model that has a plurality of first learned embeddings respectively for a plurality of entities;
- re-training, by the computing system, the first version of the machine-learned model to obtain a second version of the machine-learned model that has a plurality of second learned embeddings respectively for the plurality of entities;
- determining, by the computing system, for each entity, a respective similarity score between the first learned embedding for the entity and the second learned embedding for the entity;
- identifying, by the computing system, a subset of the entities that have respective similarity scores that indicate relative dissimilarity between their respective embeddings;
- selecting, by the computing system and based at least in part on the identified subset of entities, training examples for inclusion in a training dataset, such that the training dataset is biased toward training examples that include one or more of the identified subset of entities;
- and
- re-training, by the computing system, the second version of the machine-learned model with the training dataset to obtain a third version of the machine-learned model having a plurality of third learned embeddings for the plurality of entities.
One or more non-transitory computer-readable media that collectively store instructions that, when executed by one or more processors cause the one or more processors to perform operations, the operations comprising:
- obtaining a first version of a machine-learned model;
- re-training the first version of the machine-learned model to obtain a second version of the machine-learned model;
- processing a plurality of training examples with the first version of the machine-learned model to respectively obtain a plurality of first embeddings generated by the first version of the machine-learned model respectively for the plurality of training examples;
- processing the plurality of training examples with the second version of the machine-learned model to respectively obtain a plurality of second embeddings generated by the second version of the machine-learned model respectively for the plurality of training examples;
- determining, for each of the plurality of training examples, a respective similarity score between the first embedding generated for the training example by the first version of the machine-learned model and the second embedding generated for the training example by the second version of the machine-learned model;
- selecting, based at least in part on the similarity scores, training examples for inclusion in a training dataset, such that the training dataset is biased toward training examples that have respective similarity scores that indicate relative dissimilarity between their respective embeddings;
- re-training the second version of the machine-learned model with the training dataset to obtain a third version of the machine-learned model.
A computing system configured to perform online hard example mining for an actively deployed machine-learned model, the computing system comprising:
- one or more processors;
- and
- one or more non-transitory computer-readable media that collectively store instructions that, when executed by one or more processors, cause the computing system to perform operations, the operations comprising:
- deploying a machine-learned model to perform a task;
- performing online learning to re-train the machine-learned model with online training examples while the machine-learned model is deployed to perform the task;
- maintaining, as part of performing online learning, a log of respective loss values exhibited by the machine-learned model for the online training examples as evaluated by a loss function;
- identifying a subset of the online training examples as hard examples based at least in part on the respective loss values exhibited by the machine-learned model for the online training examples;
- and
- re-training the machine-learned model using the identified subset of online training examples that are hard examples.
Dependencies are inferred from the claim text.