硕士生导师
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学历：博士研究生毕业
学位：工学博士学位
办公地点：犀浦3号教学楼31529
毕业院校：四川大学
学科：电子信息. 软件工程. 计算机应用技术
所在单位：计算机与人工智能学院
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The detection of distributional discrepancy for language GANs
DOI码：10.1080/09540091.2022.2080182
所属单位：西南交通大学
发表刊物：Connection Science
刊物所在地：ENGLAND
关键字：Text generation; generative adversarial nets; distributional discrepancy
摘要：A pre-trained neural language model (LM) is usually used to generate texts. Due to exposure bias, the generated text is not as good as real text. Many researchers claimed they employed the Generative Adversarial Nets (GAN) to alleviate this issue by feeding reward signals from a discriminator to update the LM (generator). However, some researchers argued that GAN did not work by evaluating the generated texts with a quality-diversity metric such as Bleu versus self-Bleu, and language model score versus reverse language model score. Unfortunately, these two-dimension metrics are not reliable. Furthermore, the existing methods only assessed the final generated texts, thus neglecting the dynamic evaluating the adversarial learning process. Different from the above-mentioned methods, we adopted the most recent metric functions, which measure the distributional discrepancy between real and generated text. Besides that, we design a comprehensive experiment to investigate the performance during the learning process. First, we evaluate a language model with two functions and identify a large discrepancy. Then, several methods with the detected discrepancy signal to improve the generator were tried. Experimenting with two language GANs on two benchmark datasets, we found that the distributional discrepancy increases with more adversarial learning rounds. Our research provides convicted evidence that the language GANs fail.
合写作者：Peng Jin,Ping Cai,Hongjun Wang,Jiajun Chen
第一作者：Xingyuan Chen
论文类型：SCI
通讯作者：Xinyu Dai
学科门类：工学
文献类型：J
卷号：34
期号：1
页面范围：1736-1750
ISSN号：0954-0091
是否译文：否
发表时间：2022-06-14
收录刊物：SCI