Considering that both the source and target sides may have monolingual data that has valuable information, one can leverage both at once via joint training on the two translation directions. This type of dual learning simultaneously improves the two models on both translation directions by aligning the original monolingual sentences $x$ and the sentences $x'$ translated forward and then backward ($x \to y' \to x'$) by the two models. Dual learning can be further improved by introducing multi-agent frameworks for both translation directions. Specific approaches for joint training include iterative back translation, bi-directional NMT, and mirror-generative NMT.