@inproceedings{jie-lu-2018-dependency,
title = "Dependency-based Hybrid Trees for Semantic Parsing",
author = "Jie, Zhanming and
Lu, Wei",
editor = "Riloff, Ellen and
Chiang, David and
Hockenmaier, Julia and
Tsujii, Jun{'}ichi",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
month = oct # "-" # nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D18-1265",
doi = "10.18653/v1/D18-1265",
pages = "2431--2441",
abstract = "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.",
}
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%0 Conference Proceedings
%T Dependency-based Hybrid Trees for Semantic Parsing
%A Jie, Zhanming
%A Lu, Wei
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F jie-lu-2018-dependency
%X 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.
%R 10.18653/v1/D18-1265
%U https://aclanthology.org/D18-1265
%U https://doi.org/10.18653/v1/D18-1265
%P 2431-2441
Markdown (Informal)
[Dependency-based Hybrid Trees for Semantic Parsing](https://aclanthology.org/D18-1265) (Jie & Lu, EMNLP 2018)
ACL
- Zhanming Jie and Wei Lu. 2018. Dependency-based Hybrid Trees for Semantic Parsing. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 2431–2441, Brussels, Belgium. Association for Computational Linguistics.