@inproceedings{ji-etal-2021-word,
title = "Word Reordering for Zero-shot Cross-lingual Structured Prediction",
author = "Ji, Tao and
Jiang, Yong and
Wang, Tao and
Huang, Zhongqiang and
Huang, Fei and
Wu, Yuanbin and
Wang, Xiaoling",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.338",
doi = "10.18653/v1/2021.emnlp-main.338",
pages = "4109--4120",
abstract = "Adapting word order from one language to another is a key problem in cross-lingual structured prediction. Current sentence encoders (e.g., RNN, Transformer with position embeddings) are usually word order sensitive. Even with uniform word form representations (MUSE, mBERT), word order discrepancies may hurt the adaptation of models. In this paper, we build structured prediction models with bag-of-words inputs, and introduce a new reordering module to organizing words following the source language order, which learns task-specific reordering strategies from a general-purpose order predictor model. Experiments on zero-shot cross-lingual dependency parsing, POS tagging, and morphological tagging show that our model can significantly improve target language performances, especially for languages that are distant from the source language.",
}
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<abstract>Adapting word order from one language to another is a key problem in cross-lingual structured prediction. Current sentence encoders (e.g., RNN, Transformer with position embeddings) are usually word order sensitive. Even with uniform word form representations (MUSE, mBERT), word order discrepancies may hurt the adaptation of models. In this paper, we build structured prediction models with bag-of-words inputs, and introduce a new reordering module to organizing words following the source language order, which learns task-specific reordering strategies from a general-purpose order predictor model. Experiments on zero-shot cross-lingual dependency parsing, POS tagging, and morphological tagging show that our model can significantly improve target language performances, especially for languages that are distant from the source language.</abstract>
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%0 Conference Proceedings
%T Word Reordering for Zero-shot Cross-lingual Structured Prediction
%A Ji, Tao
%A Jiang, Yong
%A Wang, Tao
%A Huang, Zhongqiang
%A Huang, Fei
%A Wu, Yuanbin
%A Wang, Xiaoling
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F ji-etal-2021-word
%X Adapting word order from one language to another is a key problem in cross-lingual structured prediction. Current sentence encoders (e.g., RNN, Transformer with position embeddings) are usually word order sensitive. Even with uniform word form representations (MUSE, mBERT), word order discrepancies may hurt the adaptation of models. In this paper, we build structured prediction models with bag-of-words inputs, and introduce a new reordering module to organizing words following the source language order, which learns task-specific reordering strategies from a general-purpose order predictor model. Experiments on zero-shot cross-lingual dependency parsing, POS tagging, and morphological tagging show that our model can significantly improve target language performances, especially for languages that are distant from the source language.
%R 10.18653/v1/2021.emnlp-main.338
%U https://aclanthology.org/2021.emnlp-main.338
%U https://doi.org/10.18653/v1/2021.emnlp-main.338
%P 4109-4120
Markdown (Informal)
[Word Reordering for Zero-shot Cross-lingual Structured Prediction](https://aclanthology.org/2021.emnlp-main.338) (Ji et al., EMNLP 2021)
ACL
- Tao Ji, Yong Jiang, Tao Wang, Zhongqiang Huang, Fei Huang, Yuanbin Wu, and Xiaoling Wang. 2021. Word Reordering for Zero-shot Cross-lingual Structured Prediction. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 4109–4120, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.