论文标题
插入有条件文本生成的自动编码器
Plug and Play Autoencoders for Conditional Text Generation
论文作者
论文摘要
文本自动编码器通常用于有条件的生成任务，例如样式传输。我们建议使用插件的方法，可以使用任何预处理的自动编码器，并且只需要学习自动编码器的嵌入空间中的映射，训练嵌入到嵌入式到设备（EMB2EMB）。这减少了对任务标记的培训数据的需求，并使培训程序更有效。对于此方法的成功至关重要，是将映射嵌入自动编码器的层次和映射的损失术语，该映射是通过学习偏移量向量来导航歧管的训练的映射。在有和没有顺序对序列监督的情况下，对样式转移任务的评估表明，我们的方法的性能优于强度或与强基础相当，而最多可快四倍。
Text autoencoders are commonly used for conditional generation tasks such as style transfer. We propose methods which are plug and play, where any pretrained autoencoder can be used, and only require learning a mapping within the autoencoder's embedding space, training embedding-to-embedding (Emb2Emb). This reduces the need for labeled training data for the task and makes the training procedure more efficient. Crucial to the success of this method is a loss term for keeping the mapped embedding on the manifold of the autoencoder and a mapping which is trained to navigate the manifold by learning offset vectors. Evaluations on style transfer tasks both with and without sequence-to-sequence supervision show that our method performs better than or comparable to strong baselines while being up to four times faster.
