  Humans can systematically generalize to novel compositions of existing
concepts. Recent studies argue that neural networks appear inherently
ineffective in such cognitive capacity, leading to a pessimistic view and a
lack of attention to optimistic results. We revisit this controversial topic
from the perspective of meaningful learning, an exceptional capability of
humans to learn novel concepts by connecting them with known ones. We reassess
the compositional skills of sequence-to-sequence models conditioned on the
semantic links between new and old concepts. Our observations suggest that
models can successfully one-shot generalize to novel concepts and compositions
through semantic linking, either inductively or deductively. We demonstrate
that prior knowledge plays a key role as well. In addition to synthetic tests,
we further conduct proof-of-concept experiments in machine translation and
semantic parsing, showing the benefits of meaningful learning in applications.
We hope our positive findings will encourage excavating modern neural networks'
potential in systematic generalization through more advanced learning schemes.
