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We present a noisy channel generative model of two sequences, for exampletext and speech, which enables uncovering the association between the twomodalities when limited paired data is available. To address the intractabilityof the exact model under a realistic data setup, we propose a variationalinference approximation. To train this variational model with categorical data,we propose a KL encoder loss approach which has connections to the wake-sleepalgorithm. Identifying the joint or conditional distributions by only observingunpaired samples from the marginals is only possible under certain conditionsin the data distribution and we discuss under what type of conditionalindependence assumptions that might be achieved, which guides the architecturedesigns. Experimental results show that even tiny amount of paired data (5minutes) is sufficient to learn to relate the two modalities (graphemes andphonemes here) when a massive amount of unpaired data is available, paving thepath to adopting this principled approach for all seq2seq models in low dataresource regimes.

Learning the joint distribution of two sequences using little or no paired data