A Simple and Effective Method to Improve Zero-Shot Cross-Lingual Transfer Learning
Kunbo Ding, Weijie Liu, Yuejian Fang, Weiquan Mao, Zhe Zhao, Tao Zhu, Haoyan Liu, Rong Tian, Yiren Chen
Correct Metadata for
Abstract
Existing zero-shot cross-lingual transfer methods rely on parallel corpora or bilingual dictionaries, which are expensive and impractical for low-resource languages. To disengage from these dependencies, researchers have explored training multilingual models on English-only resources and transferring them to low-resource languages. However, its effect is limited by the gap between embedding clusters of different languages. To address this issue, we propose Embedding-Push, Attention-Pull, and Robust targets to transfer English embeddings to virtual multilingual embeddings without semantic loss, thereby improving cross-lingual transferability. Experimental results on mBERT and XLM-R demonstrate that our method significantly outperforms previous works on the zero-shot cross-lingual text classification task and can obtain a better multilingual alignment.- Anthology ID:
- 2022.coling-1.385
- Volume:
- Proceedings of the 29th International Conference on Computational Linguistics
- Month:
- October
- Year:
- 2022
- Address:
- Gyeongju, Republic of Korea
- Editors:
- Nicoletta Calzolari, Chu-Ren Huang, Hansaem Kim, James Pustejovsky, Leo Wanner, Key-Sun Choi, Pum-Mo Ryu, Hsin-Hsi Chen, Lucia Donatelli, Heng Ji, Sadao Kurohashi, Patrizia Paggio, Nianwen Xue, Seokhwan Kim, Younggyun Hahm, Zhong He, Tony Kyungil Lee, Enrico Santus, Francis Bond, Seung-Hoon Na
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 4372–4380
- Language:
- URL:
- https://aclanthology.org/2022.coling-1.385/
- DOI:
- Bibkey:
- Cite (ACL):
- Kunbo Ding, Weijie Liu, Yuejian Fang, Weiquan Mao, Zhe Zhao, Tao Zhu, Haoyan Liu, Rong Tian, and Yiren Chen. 2022. A Simple and Effective Method to Improve Zero-Shot Cross-Lingual Transfer Learning. In Proceedings of the 29th International Conference on Computational Linguistics, pages 4372–4380, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
- Cite (Informal):
- A Simple and Effective Method to Improve Zero-Shot Cross-Lingual Transfer Learning (Ding et al., COLING 2022)
- Copy Citation:
- PDF:
- https://aclanthology.org/2022.coling-1.385.pdf
- Code
- kb-ding/ear
- Data
- XNLI
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@inproceedings{ding-etal-2022-simple,
title = "A Simple and Effective Method to Improve Zero-Shot Cross-Lingual Transfer Learning",
author = "Ding, Kunbo and
Liu, Weijie and
Fang, Yuejian and
Mao, Weiquan and
Zhao, Zhe and
Zhu, Tao and
Liu, Haoyan and
Tian, Rong and
Chen, Yiren",
editor = "Calzolari, Nicoletta and
Huang, Chu-Ren and
Kim, Hansaem and
Pustejovsky, James and
Wanner, Leo and
Choi, Key-Sun and
Ryu, Pum-Mo and
Chen, Hsin-Hsi and
Donatelli, Lucia and
Ji, Heng and
Kurohashi, Sadao and
Paggio, Patrizia and
Xue, Nianwen and
Kim, Seokhwan and
Hahm, Younggyun and
He, Zhong and
Lee, Tony Kyungil and
Santus, Enrico and
Bond, Francis and
Na, Seung-Hoon",
booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2022.coling-1.385/",
pages = "4372--4380",
abstract = "Existing zero-shot cross-lingual transfer methods rely on parallel corpora or bilingual dictionaries, which are expensive and impractical for low-resource languages. To disengage from these dependencies, researchers have explored training multilingual models on English-only resources and transferring them to low-resource languages. However, its effect is limited by the gap between embedding clusters of different languages. To address this issue, we propose Embedding-Push, Attention-Pull, and Robust targets to transfer English embeddings to virtual multilingual embeddings without semantic loss, thereby improving cross-lingual transferability. Experimental results on mBERT and XLM-R demonstrate that our method significantly outperforms previous works on the zero-shot cross-lingual text classification task and can obtain a better multilingual alignment."
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%0 Conference Proceedings %T A Simple and Effective Method to Improve Zero-Shot Cross-Lingual Transfer Learning %A Ding, Kunbo %A Liu, Weijie %A Fang, Yuejian %A Mao, Weiquan %A Zhao, Zhe %A Zhu, Tao %A Liu, Haoyan %A Tian, Rong %A Chen, Yiren %Y Calzolari, Nicoletta %Y Huang, Chu-Ren %Y Kim, Hansaem %Y Pustejovsky, James %Y Wanner, Leo %Y Choi, Key-Sun %Y Ryu, Pum-Mo %Y Chen, Hsin-Hsi %Y Donatelli, Lucia %Y Ji, Heng %Y Kurohashi, Sadao %Y Paggio, Patrizia %Y Xue, Nianwen %Y Kim, Seokhwan %Y Hahm, Younggyun %Y He, Zhong %Y Lee, Tony Kyungil %Y Santus, Enrico %Y Bond, Francis %Y Na, Seung-Hoon %S Proceedings of the 29th International Conference on Computational Linguistics %D 2022 %8 October %I International Committee on Computational Linguistics %C Gyeongju, Republic of Korea %F ding-etal-2022-simple %X Existing zero-shot cross-lingual transfer methods rely on parallel corpora or bilingual dictionaries, which are expensive and impractical for low-resource languages. To disengage from these dependencies, researchers have explored training multilingual models on English-only resources and transferring them to low-resource languages. However, its effect is limited by the gap between embedding clusters of different languages. To address this issue, we propose Embedding-Push, Attention-Pull, and Robust targets to transfer English embeddings to virtual multilingual embeddings without semantic loss, thereby improving cross-lingual transferability. Experimental results on mBERT and XLM-R demonstrate that our method significantly outperforms previous works on the zero-shot cross-lingual text classification task and can obtain a better multilingual alignment. %U https://aclanthology.org/2022.coling-1.385/ %P 4372-4380
Markdown (Informal)
[A Simple and Effective Method to Improve Zero-Shot Cross-Lingual Transfer Learning](https://aclanthology.org/2022.coling-1.385/) (Ding et al., COLING 2022)
- A Simple and Effective Method to Improve Zero-Shot Cross-Lingual Transfer Learning (Ding et al., COLING 2022)
ACL
- Kunbo Ding, Weijie Liu, Yuejian Fang, Weiquan Mao, Zhe Zhao, Tao Zhu, Haoyan Liu, Rong Tian, and Yiren Chen. 2022. A Simple and Effective Method to Improve Zero-Shot Cross-Lingual Transfer Learning. In Proceedings of the 29th International Conference on Computational Linguistics, pages 4372–4380, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.