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We consider the problem of joint source and channel coding of structured datasuch as natural language over a noisy channel. The typical approach to thisproblem in both theory and practice involves performing source coding to firstcompress the text and then channel coding to add robustness for thetransmission across the channel. This approach is optimal in terms ofminimizing end-to-end distortion with arbitrarily large block lengths of boththe source and channel codes when transmission is over discrete memorylesschannels. However, the optimality of this approach is no longer ensured fordocuments of finite length and limitations on the length of the encoding. Wewill show in this scenario that we can achieve lower word error rates bydeveloping a deep learning based encoder and decoder. While the approach ofseparate source and channel coding would minimize bit error rates, our approachpreserves semantic information of sentences by first embedding sentences in asemantic space where sentences closer in meaning are located closer together,and then performing joint source and channel coding on these embeddings.

Deep Learning for Joint Source-Channel Coding of Text