TY - JOUR AU - Hsu, Shiou Tian AU - Moon, Changsung AU - Jones, Paul AU - Samatova, Nagiza PY - 2018/04/27 Y2 - 2024/03/28 TI - An Interpretable Generative Adversarial Approach to Classification of Latent Entity Relations in Unstructured Sentences JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 32 IS - 1 SE - Main Track: NLP and Machine Learning DO - 10.1609/aaai.v32i1.11972 UR - https://ojs.aaai.org/index.php/AAAI/article/view/11972 SP - AB - <p> 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. </p> ER -