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 [BibTeX] [Marc21]
Bag-of-Vectors Autoencoders for Unsupervised Conditional Text Generation
Type of publication: Conference paper
Citation: Mai_AACL_2022
Publication status: Published
Booktitle: Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
Year: 2022
Pages: 468–488
Location: Online
Organization: Association for Computational Linguistics
Crossref: Mai_Idiap-RR-21-2021:
URL: https://aclanthology.org/2022....
Abstract: 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.
Projects LAOS
Authors Mai, Florian
Henderson, James
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