Abstract
In 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.- Anthology ID:
- R17-1059
- Volume:
- Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017
- Month:
- September
- Year:
- 2017
- Address:
- Varna, Bulgaria
- Editors:
- Ruslan Mitkov, Galia Angelova
- Venue:
- RANLP
- SIG:
- Publisher:
- INCOMA Ltd.
- Note:
- Pages:
- 446–451
- Language:
- URL:
- https://doi.org/10.26615/978-954-452-049-6_059
- DOI:
- 10.26615/978-954-452-049-6_059
- Cite (ACL):
- Zhihua Liao and Yan Xie. 2017. A Statistical Machine Translation Model with Forest-to-Tree Algorithm for Semantic Parsing. In Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017, pages 446–451, Varna, Bulgaria. INCOMA Ltd..
- Cite (Informal):
- A Statistical Machine Translation Model with Forest-to-Tree Algorithm for Semantic Parsing (Liao & Xie, RANLP 2017)
- PDF:
- https://doi.org/10.26615/978-954-452-049-6_059