论文标题
层次poset解码用于语言的组成概括
Hierarchical Poset Decoding for Compositional Generalization in Language
论文作者
论文摘要
我们将人类语言理解形式化为结构化的预测任务，其中输出为部分有序集（POSET）。当前的编码器架构无法正确考虑语义的POSET结构，因此遭受了不良的组成概括能力。在本文中，我们提出了一种新型的层次poset解码范式，用于语言中的组成概括。直觉：（1）拟议的范式在语义中强制执行部分置换不变，从而避免过度适合偏见订购信息； （2）分层机制允许捕获POSET的高级结构。我们评估了我们提出的解码器关于组合式封闭问题（CFQ），这是一个大型逼真的自然语言问题，回答数据集，专门设计用于衡量组成概括。结果表明，它的表现优于当前解码器。
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.
