We propose a novel dependency-based hybrid tree model for semantic parsing,
which converts natural language utterance into machine interpretable meaning
representations. Unlike previous state-of-the-art models, the semantic
information is interpreted as the latent dependency between the natural
language words in our joint representation. Such dependency information can
capture the interactions between the semantics and natural language words. We
integrate a neural component into our model and propose an efficient
dynamic-programming algorithm to perform tractable inference. Through extensive
experiments on the standard multilingual GeoQuery dataset with eight languages,
we demonstrate that our proposed approach is able to achieve state-of-the-art
performance across several languages. Analysis also justifies the effectiveness
of using our new dependency-based representation.

本研究提出了一种新的基于依存的混合树模型，将自然语言转换成机器可解释的含义表示，并通过在标准多语言 GeoQuery 数据集上进行的大量实验证明，我们的方法能够在多种语言上实现最先进的性能。