Coupled Generation

Abstract

Instance generation creates representative examples to interpret a learning model, as in regression and classification. For example, representative sentences of a topic of interest describe the topic specifically for sentence categorization. In such a situation, a large number of unlabeled observations may be available in addition to labeled data, for example, many unclassified text corpora (unlabeled instances) are available with only a few classified sentences (labeled instances). In this article, we introduce a novel generative method, called a coupled generator, producing instances given a specific learning outcome, based on indirect and direct generators. The indirect generator uses the inverse principle to yield the corresponding inverse probability, enabling to generate instances by leveraging an unlabeled data. The direct generator learns the distribution of an instance given its learning outcome. Then, the coupled generator seeks the best one from the indirect and direct generators, which is designed to enjoy the benefits of both and deliver higher generation accuracy. For sentence generation given a topic, we develop an embedding-based regression/classification in conjuncture with an unconditional recurrent neural network for the indirect generator, whereas a conditional recurrent neural network is natural for the corresponding direct generator. Moreover, we derive finite-sample generation error bounds for the indirect and direct generators to reveal the generative aspects of both methods thus explaining the benefits of the coupled generator. Finally, we apply the proposed methods to a real benchmark of abstract classification and demonstrate that the coupled generator composes reasonably good sentences from a dictionary to describe a specific topic of interest. Supplementary materials for this article are available online.

Keywords:

• Classification
• Natural language processing
• Numerical embeddings
• Semisupervised generation
• Unstructured data

Supplementary Materials

The supplementary materials provide Python codes used in real data application.

Acknowledgments

The authors thank the editors, the associate editor, and two anonymous referees for helpful comments and suggestions.

Notes

1 https://archive.ics.uci.edu/ml/datasets/Sentence+Classification

2 https://archive.ics.uci.edu/ml/datasets/Sentence+Classification

3 https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Ridge.html

4 https://radimrehurek.com/gensim/models/doc2vec.html

5 https://keras.io/

6 http://www.grammarcheckforsentence.com/

Additional information

Funding

Research supported in part by NSF grants DMS-1721550, DMS-1712564, DMS-1721216, DMS-1952539, DMS-1952386, and NIH grants 1R01GM126002, R01HL105397, R01AG069895, and R01AG065636.