Systems and methods for natural language processing using joint energy-based models
Embodiments described herein provide natural language processing (NLP) systems and methods that utilize energy-based models (EBMs) to compute an exponentially-weighted energy-like term in the loss function to train an NLP classifier. Specifically, noise contrastive estimation (NCE) procedures are applied together with the EBM-based loss objectives for training the NLPs.
1. A method for training a natural language processing (NLP) classifier, comprising:
receiving, via a data interface, a training dataset of data samples that correspond to a data probability distribution;
generating, for at least one data sample from the training dataset, a respective noise sample according to a noise probability distribution;
inputting a data sample and the respective noise sample to the NLP classifier;
encoding the respective data sample into an encoded data sample representation;
encoding the respective noise sample into an encoded noise sample representation;
generating, by the NLP classifier, a first classification output corresponding to the encoded data sample representation and a second classification output corresponding to the encoded noise sample representation;
computing a first energy term based at least in part on the first classification output and the encoded data sample representation according to an energy function selected from the group consisting of a scalar function, a hidden function, and a sharp-hidden function;
computing a second energy term based at least in part on the second classification output and the encoded noise sample representation according to the energy function;
computing a noise contrastive estimation (NCE) loss objective based at least in part on the first energy term and the second energy term; and
training the NLP classifier based at least in part on a combination of the NCE loss objective and a cross-entropy loss computed based on the first classification output conditioned on a respective data input sample.
2. The method of claim 1 , wherein the first energy term is computed according to the scalar function by a linear layer transformation of the encoded data sample representation.
3. The method of claim 1 , wherein the first energy term is computed according to the hidden function by applying a multivariable softplus transformation to a plurality of logits of the first classification output.
4. The method of claim 1 , wherein the first energy term is computed according to the sharp-hidden function by applying a negative maximum transformation to at least a plurality of logits of the first classification output.
5. The method of claim 1 , wherein the NCE loss objective is computed by:
computing a first expectation of a first weighted softplus component based on the first energy term, wherein the first expectation is taken over the data distribution;
computing a second expectation of a second weighted softplus component based on the second energy term, wherein the second expectation is taken over the noise distribution; and
computing a weighted sum of the first expectation and the second expectation.
6. A system for training a natural language processing (NLP) classifier, comprising:
a non-transitory memory; and
one or more processor coupled to the non-transitory memory and configured to read instructions from the non-transitory memory to cause the system to perform operations comprising:
receiving, via a data interface, a training dataset of data samples that correspond to a data probability distribution;
generating, for at least one data sample from the training dataset, a respective noise sample according to a noise probability distribution;
inputting a data sample and the respective noise sample to the NLP classifier;
encoding the respective data sample into an encoded data sample representation;
encoding the respective noise sample into an encoded noise sample representation;
generating, by the NLP classifier, a first classification output corresponding to the encoded data sample representation and a second classification output corresponding to the encoded noise sample representation;
computing a first energy term based at least in part on the first classification output and the encoded data sample representation according to an energy function selected from the group consisting of a scalar function, a hidden function, and a sharp-hidden function;
computing a second energy term based at least in part on the second classification output and the encoded noise sample representation according to the energy function;
computing a noise contrastive estimation (NCE) loss objective based at least in part on the first energy term and the second energy term; and
training the NLP classifier based at least in part on a combination of the NCE loss objective and a cross-entropy loss computed based on the first classification output conditioned on a respective data input sample.
7. The system of claim 6 , wherein the first energy term is computed according to the scalar function by a linear layer transformation of the encoded data sample representation.
8. The system of claim 6 , wherein the first energy term is computed according to the hidden function by applying a multivariable softplus transformation to a plurality of logits of the first classification output.
9. The system of claim 6 , wherein the first energy term is computed according to the sharp-hidden function by applying a negative maximum transformation to at least a plurality of logits of the first classification output.
10. The system of claim 6 , wherein the NCE loss objective is computed by:
computing a first expectation of a first weighted softplus component based on the first energy term, wherein the first expectation is taken over the data distribution;
computing a second expectation of a second weighted softplus component based on the second energy term, wherein the second expectation is taken over the noise distribution; and
computing a weighted sum of the first expectation and the second expectation.
11. A non-transitory, machine-readable medium having stored thereon machine-readable instructions executable to cause a system to perform operations comprising:
receiving, via a data interface, a training dataset of data samples that correspond to a data probability distribution;
generating, for at least one data sample from the training dataset, a respective noise sample according to a noise probability distribution;
inputting a data sample and the respective noise sample to a natural language processing (NLP) classifier;
encoding the respective data sample into an encoded data sample representation;
encoding the respective noise sample into an encoded noise sample representation;
generating, by the NLP classifier, a first classification output corresponding to the encoded data sample representation and a second classification output corresponding to the encoded noise sample representation;
computing a first energy term based at least in part on the first classification output and the encoded data sample representation according to an energy function selected from the group consisting of a scalar function, a hidden function, and a sharp-hidden function;
computing a second energy term based at least in part on the second classification output and the encoded noise sample representation according to the energy function;
computing a noise contrastive estimation (NCE) loss objective based at least in part on the first energy term and the second energy term; and
training the NLP classifier based at least in part on a combination of the NCE loss objective and a cross-entropy loss computed based on the first classification output conditioned on a respective data input sample.
12. The non-transitory, machine-readable medium of claim 11 , wherein the first energy term is computed according to the scalar function by a linear layer transformation of the encoded data sample representation.
13. The non-transitory, machine-readable medium of claim 11 , wherein the first energy term is computed according to the hidden function by applying a multivariable softplus transformation to a plurality of logits of the first classification output.
14. The non-transitory, machine-readable medium of claim 11 , wherein the first energy term is computed according to the sharp-hidden function by applying a negative maximum transformation to at least a plurality of logits of the first classification output.
15. The non-transitory, machine-readable medium of claim 11 , wherein the NCE loss objective is computed by:
computing a first expectation of a first weighted softplus component based on the first energy term, wherein the first expectation is taken over the data distribution;
computing a second expectation of a second weighted softplus component based on the second energy term, wherein the second expectation is taken over the noise distribution; and
computing a weighted sum of the first expectation and the second expectation.