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

Zhihua Liao, Yan Xie


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
Bibkey:
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)
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PDF:
https://doi.org/10.26615/978-954-452-049-6_059