Text autoencoders are often used for unsupervised conditional text generation by applying mappings in the latent space to change attributes to the desired values. Recently, Mai et al. (2020) proposed Emb2Emb, a method to learn these mappings in the embedding space of an autoencoder. However, their method is restricted to autoencoders with a single-vector embedding, which limits how much information can be retained. We address this issue by extending their method to Bag-of-Vectors Autoencoders (BoV-AEs), which encode the text into a variable-size bag of vectors that grows with the size of the text, as in attention-based models. This allows to encode and reconstruct much longer texts than standard autoencoders. Analogous to conventional autoencoders, we propose regularization techniques that facilitate learning meaningful operations in the latent space. Finally, we adapt Emb2Emb for a training scheme that learns to map an input bag to an output bag, including a novel loss function and neural architecture. Our empirical evaluations on unsupervised sentiment transfer show that our method performs substantially better than a standard autoencoder.

翻译：文本自编码器常通过潜在空间中的映射操作改变属性至期望值，从而实现无监督条件文本生成。近期Mai等人(2020)提出的Emb2Emb方法可在自编码器的嵌入空间中学习此类映射。然而该方法受限于单向量嵌入的自编码器，限制了信息保留能力。我们通过将其扩展至词袋向量自编码器（BoV-AEs）解决此问题，该模型可将文本编码为随文本长度增长的变长词袋向量，其机制与基于注意力的模型类似。这使得相较于标准自编码器，可编码并重构更长的文本。参照传统自编码器，我们提出促进在潜在空间学习有意义的操作的规范化技术。最后，我们改进Emb2Emb训练方案，使其能够学习将输入词袋映射至输出词袋，包括创新的损失函数与神经网络架构。在无监督情感迁移任务上的实证评估表明，我们的方法显著优于标准自编码器。