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• W2949380104 abstract "In this work, we focus on a lightweight convolutional architecture that creates fixed-size vector embeddings of sentences. Such representations are useful for building NLP systems, including conversational agents. Our work derives from a recently proposed recursive convolutional architecture for auto-encoding text paragraphs at byte level. We propose alternations that significantly reduce training time, the number of parameters, and improve auto-encoding accuracy. Finally, we evaluate the representations created by our model on tasks from SentEval benchmark suite, and show that it can serve as a better, yet fairly low-resource alternative to popular bag-of-words embeddings." @default.
• W2949380104 created "2019-06-27" @default.
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• W2949380104 date "2018-08-01" @default.
• W2949380104 modified "2023-09-22" @default.
• W2949380104 title "Efficient Purely Convolutional Text Encoding" @default.
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• W2949380104 hasPublicationYear "2018" @default.
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