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Title: Target Language-Aware Constrained Inference for Cross-lingual Dependency Parsing
Prior work on cross-lingual dependency parsing often focuses on capturing the commonalities between source and target languages and overlook the potential to leverage the linguistic properties of the target languages to facilitate the transfer. In this paper, we show that weak supervisions of linguistic knowledge for the target languages can improve a cross-lingual graph-based dependency parser substantially. Specifically, we explore several types of corpus linguistic statistics and compile them into corpus-statistics constraints to facilitate the inference procedure. We propose new algorithms that adapt two techniques, Lagrangian relaxation and posterior regularization, to conduct inference with corpus-statistics constraints. Experiments show that the Lagrangian relaxation and posterior regularization techniques improve the performances on 15 and 17 out of 19 target languages, respectively. The improvements are especially large for the target languages that have different word order features from the source language.  more » « less
Award ID(s):
1760523
NSF-PAR ID:
10144862
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Page Range / eLocation ID:
1117 to 1128
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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