After learning how to "dax," people understand how to "dax twice," "dax slowly," or even "dax like there is no tomorrow." These abilities are central to language and thought yet they are conspicuously lacking in modern neural networks<cite> [15,</cite> 3, 20, 22, 2] . In this paper, I introduced a meta sequence-to-sequence (meta seq2seq) approach for learning to generalize compositionally, exploiting the algebraic structure of a domain to help understand novel utterances. In contrast to standard seq2seq, meta seq2seq learners can abstract away the surface patterns and operate closer to rule space.