[3O1-OS-2c] 人間と共生する対話知能

[3O1-OS-2c-05] Post-editing factual inconsistency using joint learning

〇Keita Moriwaki¹, Ishigaki Ryoma¹, Hiroaki Sugiyama², Masaki Shuzo¹, Eisaku Maeda¹ (1. Tokyo Denki University, 2. NTT Communication Science Laboratories)

Keywords:factual inconsistency , joint learning

Recent models of sentence generation using neural networks have become capable of generating sentences
that are as natural as those written by humans. However, the generated sentences may contain factual inconsistencies, which is problematic from a practical standpoint. Studies have been conducted to correct factual inconsistencies using a network that combines a modifier model with another functional model. In such networks, problems have been pointed out, such as errors in one model propagating to the other model. In this study, inspired by GAN (Generative Adversarial Network), we propose a joint learning method that uses corrector to edit factual inaccuracies and a discriminator to detect them. The output of the neural sentence generation model was used to validate the effectiveness of the method.