An Interpretable Generative Adversarial Approach to Classification of Latent Entity Relations in Unstructured Sentences

Authors

  • Shiou Tian Hsu North Carolina State University
  • Changsung Moon North Carolina State University
  • Paul Jones North Carolina State University
  • Nagiza Samatova North Carolina State University

Keywords:

Relation Classification

Abstract

We propose a generative adversarial neural network model for relation classification that attempts to emulate the way in which human analysts might process sentences. Our approach provides two unique benefits over existing capabilities: (1) we make predictions by finding and exploiting supportive rationales to improve interpretability (i.e. words or phrases extracted from a sentence that a person can reason upon), and (2) we allow predictions to be easily corrected by adjusting the rationales.Our model consists of three stages: Generator, Selector, and Encoder. The Generator identifies candidate text fragments; the Selector decides which fragments can be used as rationales depending on the goal; and finally, the Encoder performs relation reasoning on the rationales. While the Encoder is trained in a supervised manner to classify relations, the Generator and Selector are designed as unsupervised models to identify rationales without prior knowledge, although they can be semi-supervised through human annotations. We evaluate our model on data from SemEval 2010 that provides 19 relation-classes. Experiments demonstrate that our approach outperforms state-of-the-art models, and that our model is capable of extracting good rationales on its own as well as benefiting from labeled rationales if provided.

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Published

2018-04-27

How to Cite

Hsu, S. T., Moon, C., Jones, P., & Samatova, N. (2018). An Interpretable Generative Adversarial Approach to Classification of Latent Entity Relations in Unstructured Sentences. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/11972