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We train multi-task autoencoders on linguistic tasks and analyze the learnedhidden sentence representations. The representations change significantly whentranslation and part-of-speech decoders are added. The more decoders a modelemploys, the better it clusters sentences according to their syntacticsimilarity, as the representation space becomes less entangled. We explore thestructure of the representation space by interpolating between sentences, whichyields interesting pseudo-English sentences, many of which have recognizablesyntactic structure. Lastly, we point out an interesting property of ourmodels: The difference-vector between two sentences can be added to change athird sentence with similar features in a meaningful way.

Natural Language Multitasking: Analyzing and Improving Syntactic Saliency of Hidden Representations