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    <abstract>We propose a novel approach to cross-lingual dependency parsing based on word reordering. The words in each sentence of a source language corpus are rearranged to meet the word order in a target language under the guidance of a part-of-speech based language model (LM). To obtain the highest reordering score under the LM, a population-based optimization algorithm and its genetic operators are designed to deal with the combinatorial nature of such word reordering. A parser trained on the reordered corpus then can be used to parse sentences in the target language. We demonstrate through extensive experimentation that our approach achieves better or comparable results across 25 target languages (1.73% increase in average), and outperforms a baseline by a significant margin on the languages that are greatly different from the source one. For example, when transferring the English parser to Hindi and Latin, our approach outperforms the baseline by 15.3% and 6.7% respectively.</abstract>
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%0 Conference Proceedings
%T Cross-Lingual Dependency Parsing by POS-Guided Word Reordering
%A Liu, Lu
%A Zhou, Yi
%A Xu, Jianhan
%A Zheng, Xiaoqing
%A Chang, Kai-Wei
%A Huang, Xuanjing
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Findings of the Association for Computational Linguistics: EMNLP 2020
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F liu-etal-2020-cross-lingual-dependency
%X We propose a novel approach to cross-lingual dependency parsing based on word reordering. The words in each sentence of a source language corpus are rearranged to meet the word order in a target language under the guidance of a part-of-speech based language model (LM). To obtain the highest reordering score under the LM, a population-based optimization algorithm and its genetic operators are designed to deal with the combinatorial nature of such word reordering. A parser trained on the reordered corpus then can be used to parse sentences in the target language. We demonstrate through extensive experimentation that our approach achieves better or comparable results across 25 target languages (1.73% increase in average), and outperforms a baseline by a significant margin on the languages that are greatly different from the source one. For example, when transferring the English parser to Hindi and Latin, our approach outperforms the baseline by 15.3% and 6.7% respectively.
%R 10.18653/v1/2020.findings-emnlp.265
%U https://aclanthology.org/2020.findings-emnlp.265
%U https://doi.org/10.18653/v1/2020.findings-emnlp.265
%P 2938-2948

Markdown (Informal)

[Cross-Lingual Dependency Parsing by POS-Guided Word Reordering](https://aclanthology.org/2020.findings-emnlp.265) (Liu et al., Findings 2020)

• Cross-Lingual Dependency Parsing by POS-Guided Word Reordering (Liu et al., Findings 2020)

ACL

• Lu Liu, Yi Zhou, Jianhan Xu, Xiaoqing Zheng, Kai-Wei Chang, and Xuanjing Huang. 2020. Cross-Lingual Dependency Parsing by POS-Guided Word Reordering. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 2938–2948, Online. Association for Computational Linguistics.