Hierarchical Poset Decoding for Compositional Generalization in Language

• URL: http://arxiv.org/abs/2010.07792v1
• Date: Thu, 15 Oct 2020 14:34:26 GMT
• Title: Hierarchical Poset Decoding for Compositional Generalization in Language
• Authors: Yinuo Guo, Zeqi Lin, Jian-Guang Lou, Dongmei Zhang
• Abstract summary: 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. We propose a novel hierarchical poset decoding paradigm for compositional generalization in language.
• Score: 52.13611501363484
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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