  We formalize human language understanding as a structured prediction task
where the output is a partially ordered set (poset). Current encoder-decoder
architectures do not take the poset structure of semantics into account
properly, thus suffering from poor compositional generalization ability. In
this paper, we propose a novel hierarchical poset decoding paradigm for
compositional generalization in language. Intuitively: (1) the proposed
paradigm enforces partial permutation invariance in semantics, thus avoiding
overfitting to bias ordering information; (2) the hierarchical mechanism allows
to capture high-level structures of posets. We evaluate our proposed decoder on
Compositional Freebase Questions (CFQ), a large and realistic natural language
question answering dataset that is specifically designed to measure
compositional generalization. Results show that it outperforms current
decoders.
