Hierarchical Poset Decoding for Compositional Generalization in Language
Yinuo Guo, Zeqi Lin, Jian–Guang Lou, Dongmei Zhang
Abstract
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.