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• W2964331031 abstract "We train multi-task autoencoders on linguistic tasks and analyze the learned hidden sentence representations. The representations change significantly when translation and part-of-speech decoders are added. The more decoders a model employs, the better it clusters sentences according to their syntactic similarity, as the representation space becomes less entangled. We explore the structure of the representation space by interpolating between sentences, which yields interesting pseudo-English sentences, many of which have recognizable syntactic structure. Lastly, we point out an interesting property of our models: The difference-vector between two sentences can be added to change a third sentence with similar features in a meaningful way." @default.
• W2964331031 created "2019-07-30" @default.
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• W2964331031 date "2018-01-01" @default.
• W2964331031 modified "2023-09-24" @default.
• W2964331031 title "Natural Language Multitasking - Analyzing and Improving Syntactic Saliency of Hidden Representations" @default.
• W2964331031 hasPublicationYear "2018" @default.
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