A Statistical Machine Translation Model with Forest-to-Tree Algorithm for Semantic Parsing
20411Authors
nan
Abstract
AbstractIn this paper, we propose a novel supervised model for parsing natural language sentences into their formal semantic representations. This model treats sentence-to-lambda-logical expression conversion within the framework of the statistical machine translation with forest-to-tree algorithm. To make this work, we transform the lambda-logical 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.
Keywords
Full Content
nan
Labeled Text
AbstractIn this paper, we propose a novel supervised model for parsing natural language sentences into their formal semantic representations. This model treats sentence-to-lambda-logical expression conversion within the framework of the statistical machine translation with forest-to-tree algorithm. To make this work, we transform the lambda-logical 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.
Publication Details
- Paper ID:
- 20411
- Year:
- 2017
- Publication:
- TIPSTER
- Publication Type:
- conf
- DOI:
- nan