Out-of-distribution prediction
AssigneeCitigroup Inc.
InventorsAlberto Cetoli, Minjeong Cho, Jason Ryan Engelbrecht, Ines Teixeira
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Abstract
A set of features of a training document are identified in a training document for training a machine learning model. A subset of the features is selected to be omitted from a training forward propagation. As a result of omitting the subset of the set of features, a different subset of the set of features is used to train the machine learning model to classify documents and distinguish between an out-of-domain document and in-domain document.
Claims22
A system for detecting out-of-domain documents, comprising:
- one or more processors;
- and
- memory storing computer-executable code that, as a result of execution by the one or more processors, cause the system to at least:
- identify a common feature between both an out-of-domain machine learning training document and an in-domain machine learning training document;
- extract a set of features for at least one machine learning training forward propagation, the set of features including features of the out-of-domain machine learning training document and the in-domain machine learning training document;
- mask the common feature so as to exclude at least part of the common feature from the set of features, wherein masking the common feature comprises tokenizing and modifying the common feature to perform attention masking on at least part of the set of features corresponding to the common feature;
- and
- train, using the set of features that exclude at least part of the common feature in the at least one machine learning training forward propagation, a machine learning model to produce a trained machine learning model that predicts whether an input document is out-of-domain.
A computer-implemented method, comprising:
- identifying a common feature to both an out-of-domain document and an in-domain document;
- selecting a set of features for at least one training forward propagation, the set of features of both the out-of-domain document and the in-domain document;
- masking the common feature so as to exclude at least part of the common feature from the set of features, wherein masking the common feature comprises tokenizing and modifying the common feature to perform attention masking on at least part of the set of features corresponding to the common feature;
- and
- training, using the set of features that exclude at least part of the common feature in the at least one training forward propagation, a machine learning model to produce a trained machine learning model that classifies documents and distinguishes between OOD documents and in-domain documents.
A non-transitory computer-readable storage medium storing computer-executable instructions that, as a result of being executed by one or more processors of a computer system, cause the computer system to at least:
- identify a feature common to both an out-of-domain (OOD) training document and an in-domain training document;
- select a set of features for at least one machine learning training forward propagation, the set of features including features of both the out-of-domain (OOD) training document and the in-domain training document
- mask the feature common to both so as to exclude at least part of the feature common to both from the set of features, wherein masking the feature common to both comprises tokenizing and modifying the feature common to both to perform attention masking on at least part of the set of features corresponding to the feature common to both;
- and
- train, using the set of features that exclude at least part of the feature common to both in the at least one machine learning training forward propagation, a machine learning model that distinguishes between OOD documents and an in-domain documents.
Dependencies are inferred from the claim text.