Non-parallel text style transfer has attracted increasing research interests in recent years. Despite successes in transferring the style based on the encoder-decoder framework, current approaches still lack the ability to preserve the content and even logic of original sentences, mainly due to the large unconstrained model space or too simplified assumptions on latent embedding space. Since language itself is an intelligent product of humans with certain grammars and has a limited rule-based model space by its nature, relieving this problem requires reconciling the model capacity of deep neural networks with the intrinsic model constraints from human linguistic rules. To this end, we propose a method called Graph Transformer based Auto Encoder (GTAE), which models a sentence as a linguistic graph and performs feature extraction and style transfer at the graph level, to maximally retain the content and the linguistic structure of original sentences. Quantitative experiment results on three non-parallel text style transfer tasks show that our model outperforms state-of-the-art methods in content preservation, while achieving comparable performance on transfer accuracy and sentence naturalness.
翻译:近些年来,非平行文本样式的传输吸引了越来越多的研究兴趣。尽管在根据编码器-编码器框架转移风格方面取得了成功,但目前的做法仍然缺乏保存原句内容甚至逻辑的能力,主要原因是大量未受限制的模型空间或对潜在嵌入空间的过于简化的假设。由于语言本身是人类与某些语法的智能产物,而且基于规则的模型空间有限,因此缓解这一问题需要调和深层神经网络的模型能力与人类语言规则固有的模型限制。为此,我们提出了一种称为“基于图形变异器的自动编码器(GTAE)”的方法,该方法以语言图示为模型,在图形一级进行特征提取和风格转换,以尽量保留原句的内容和语言结构。关于三种非语言文本样式传输任务的量化实验结果显示,我们的模型在内容保护方面超越了最先进的方法,同时在传输精准性和自然性方面实现了类似的表现。