Papers › Hierarchical Poset Decoding for Compositional Generalization in Language

Hierarchical Poset Decoding for Compositional Generalization in Language

15 Oct 2020NeurIPS 2020 12arXiv:2010.07792archive 2025-07-28

Yinuo Guo, Zeqi Lin, Jian-Guang Lou, Dongmei Zhang

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.

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