---
title: "Joint Learning of Constituency and Dependency Grammars by Decomposed Cross-Lingual Induction"
authors:
  - "Wenbin Jiang"
  - "Qun Liu"
  - "Thepchai Supnithi"
doi: ""
url: "https://ijcai.org/proceedings/2015/139"
pdf: "https://ijcai.org/proceedings/2015/0139.pdf"
venue: "Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence"
year: 2015
pages: "953-958"
track: "Main Track"
keywords:
---

# Joint Learning of Constituency and Dependency Grammars by Decomposed Cross-Lingual Induction

Wenbin Jiang, Qun Liu, Thepchai Supnithi

*Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence*, pages 953-958.

## Abstract

Cross-lingual induction aims to acquire for one language some linguistic structures resorting to annotations from another language. It works well for simple structured predication problems such as part-of-speech tagging and dependency parsing, but lacks of significant progress for more complicated problems such as constituency parsing and deep semantic parsing, mainly due to the structural non-isomorphism between languages. We propose a decomposed projection strategy for cross-lingual induction, where cross-lingual projection is performed in unit of fundamental decisions of the structured predication. Compared with the structured projection that projects the complete structures, decomposed projection achieves better adaptation of non-isomorphism between languages and efficiently acquires the structured information across languages, thus leading to better performance. For joint cross-lingual induction of constituency and dependency grammars, decomposed cross-lingual induction achieves very significant improvement in both constituency and dependency grammar induction.