A Statistical Machine Translation Model with Forest-to-Tree Algorithm for Semantic Parsing

In this paper, we propose a novel supervised model for parsing natural language sentences into their formal semantic representations. This model treats sentenceto-λ-logical expression conversion within the framework of the statistical machine translation with forest-to-tree algorithm. To make this work, we transform the λlogical expression structure into a form suitable for the mechanics of statistical machine translation and useful for modeling. We show that our model is able to yield new state-of-the-art results on both standard datasets with simple features.