Out-of-domain data augmentation for natural language processing

A system comprising:

• one or more processors;
• and
• a memory coupled to the one or more processors, the memory storing a plurality of instructions executable by the one or more processors, the plurality of instructions, when executed by the one or more processors, cause the one or more processors to perform operations including:
• receiving a training set of utterances comprising in-domain examples;
• augmenting the training set of utterances with out-of-domain (OOD) examples to generate augmented batches of utterances for training a machine-learning model, wherein the augmenting comprises:
• generating a data set of the OOD examples,
• filtering out a plurality of OOD examples from the data set of the OOD examples, based on a determination that context of each of the plurality of OOD examples has a substantial similarity to context of one or more of the utterances of the training set of utterances, and
• generating the augmented batches of utterances, each of the augmented batches of utterances comprising utterances from the training set of utterances and utterances from the filtered data set of the OOD examples;
• and
• training the machine-learning model using the augmented batches of utterances,
• wherein the substantial similarity between the context of OOD utterances of the data set of the OOD examples and the context of the utterances of the training set is determined based on a distance measure using a Multilingual Universal Sentence Encoder (MUSE) single embedding, and
• wherein if min (d_i) is less than a predetermined threshold, then the context of an OOD utterance of the data set of the OOD examples and the context of an utterance of the training set of utterances is determined to be substantially similar,
• where d_i is an Euclidean distance (v_i, u),
• v_i is a vector representation of an utterance (x_i) of the training set of utterances and is muse(x_i) where i=1→n, and
• u is a vector representation of the OOD utterance of the data set of the OOD examples and is muse(OOD utterance).

A non-transitory computer-readable memory storing a plurality of instructions executable by one or more processors, the plurality of instructions, when executed by the one or more processors, cause the one or more processors to perform operations including:

• receiving a training set of utterances comprising in-domain examples;
• augmenting the training set of utterances with out-of-domain (OOD) examples to generate augmented batches of utterances for training a machine-learning model, wherein the augmenting comprises:
• generating a data set of the OOD examples,
• filtering out a plurality of OOD examples from the data set of the OOD examples, based on a determination that context of each of the plurality of OOD examples has a substantial similarity to context of one or more of the utterances of the training set of utterances, and
• generating the augmented batches of utterances, each of the augmented batches of utterances comprising utterances from the training set of utterances and utterances from the filtered data set of the OOD examples;
• and
• training the machine-learning model using the augmented batches of utterances,
• wherein the substantial similarity between the context of OOD utterances of the data set of the OOD examples and the context of the utterances of the training set is determined based on a distance measure using a Multilingual Universal Sentence Encoder (MUSE) single embedding, and
• wherein if min (d_i) is less than a predetermined threshold, then the context of an OOD utterance of the data set of the OOD examples and the context of an utterance of the training set of utterances is determined to be substantially similar,
• where d_i is an Euclidean distance (v_i, u),
• v_i is a vector representation of an utterance (x_i) of the training set of utterances and is muse(x_i) where i=1→n, and
• u is a vector representation of the OOD utterance of the data set of the OOD examples and is muse(OOD utterance).