Paper Info
Title
An Interpretable Generative Adversarial Approach to Classification of Latent Entity Relations in Unstructured Sentences.
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 front 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.
Year
Venue
Field
2018
THIRTY-SECOND AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE / THIRTIETH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE / EIGHTH AAAI SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE
Computer science,Natural language processing,Artificial intelligence,Generative grammar,Machine learning,Adversarial system
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Shiou Tian Hsu100.34
Changsung Moon261.79
Paul Jones373.51
Nagiza F. Samatova486174.04