Adversarial Decomposition of Text Representation
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In this paper, we present a method for adversarial decomposition of text representation. This method can be used to decompose a representation of an input sentence into several independent vectors, where each vector is responsible for a specific aspect of the input sentence. We evaluate the proposed method on several case studies: the conversion between different social registers, diachronic language change and the decomposition of the sentiment polarity of input sentences. We show that the proposed method is capable of fine-grained controlled change of these aspects of the input sentence. The model uses adversarial-motivational training and includes a special motivational loss, which acts opposite to the discriminator and encourages a better decomposition. Finally, we evaluate the obtained meaning embeddings on a downstream task of paraphrase detection and show that they are significantly better than embeddings of a regular autoencoder.
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