Unregulated Chinese-to-English Data Expansion Does NOT Work for Neural Event Detection
Zhongqiu Li, Yu Hong, Jie Wang, Shiming He, Jianmin Yao, Guodong Zhou
Correct Metadata for
Abstract
We leverage cross-language data expansion and retraining to enhance neural Event Detection (abbr., ED) on English ACE corpus. Machine translation is utilized for expanding English training set of ED from that of Chinese. However, experimental results illustrate that such strategy actually results in performance degradation. The survey of translations suggests that the mistakenly-aligned triggers in the expanded data negatively influences the retraining process. We refer this phenomenon to “trigger falsification”. To overcome the issue, we apply heuristic rules for regulating the expanded data, fixing the distracting samples that contain the falsified triggers. The supplementary experiments show that the rule-based regulation is beneficial, yielding the improvement of about 1.6% F1-score for ED. We additionally prove that, instead of transfer learning from the translated ED data, the straight data combination by random pouring surprisingly performs better.- Anthology ID:
- 2022.coling-1.232
- 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:
- 2633–2638
- Language:
- URL:
- https://aclanthology.org/2022.coling-1.232/
- DOI:
- Bibkey:
- Cite (ACL):
- Zhongqiu Li, Yu Hong, Jie Wang, Shiming He, Jianmin Yao, and Guodong Zhou. 2022. Unregulated Chinese-to-English Data Expansion Does NOT Work for Neural Event Detection. In Proceedings of the 29th International Conference on Computational Linguistics, pages 2633–2638, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
- Cite (Informal):
- Unregulated Chinese-to-English Data Expansion Does NOT Work for Neural Event Detection (Li et al., COLING 2022)
- Copy Citation:
- PDF:
- https://aclanthology.org/2022.coling-1.232.pdf
Export citation
@inproceedings{li-etal-2022-unregulated,
title = "Unregulated {C}hinese-to-{E}nglish Data Expansion Does {NOT} Work for Neural Event Detection",
author = "Li, Zhongqiu and
Hong, Yu and
Wang, Jie and
He, Shiming and
Yao, Jianmin and
Zhou, Guodong",
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-1.232/",
pages = "2633--2638",
abstract = "We leverage cross-language data expansion and retraining to enhance neural Event Detection (abbr., ED) on English ACE corpus. Machine translation is utilized for expanding English training set of ED from that of Chinese. However, experimental results illustrate that such strategy actually results in performance degradation. The survey of translations suggests that the mistakenly-aligned triggers in the expanded data negatively influences the retraining process. We refer this phenomenon to {\textquotedblleft}trigger falsification{\textquotedblright}. To overcome the issue, we apply heuristic rules for regulating the expanded data, fixing the distracting samples that contain the falsified triggers. The supplementary experiments show that the rule-based regulation is beneficial, yielding the improvement of about 1.6{\%} F1-score for ED. We additionally prove that, instead of transfer learning from the translated ED data, the straight data combination by random pouring surprisingly performs better."
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<abstract>We leverage cross-language data expansion and retraining to enhance neural Event Detection (abbr., ED) on English ACE corpus. Machine translation is utilized for expanding English training set of ED from that of Chinese. However, experimental results illustrate that such strategy actually results in performance degradation. The survey of translations suggests that the mistakenly-aligned triggers in the expanded data negatively influences the retraining process. We refer this phenomenon to “trigger falsification”. To overcome the issue, we apply heuristic rules for regulating the expanded data, fixing the distracting samples that contain the falsified triggers. The supplementary experiments show that the rule-based regulation is beneficial, yielding the improvement of about 1.6% F1-score for ED. We additionally prove that, instead of transfer learning from the translated ED data, the straight data combination by random pouring surprisingly performs better.</abstract>
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%0 Conference Proceedings %T Unregulated Chinese-to-English Data Expansion Does NOT Work for Neural Event Detection %A Li, Zhongqiu %A Hong, Yu %A Wang, Jie %A He, Shiming %A Yao, Jianmin %A Zhou, Guodong %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 li-etal-2022-unregulated %X We leverage cross-language data expansion and retraining to enhance neural Event Detection (abbr., ED) on English ACE corpus. Machine translation is utilized for expanding English training set of ED from that of Chinese. However, experimental results illustrate that such strategy actually results in performance degradation. The survey of translations suggests that the mistakenly-aligned triggers in the expanded data negatively influences the retraining process. We refer this phenomenon to “trigger falsification”. To overcome the issue, we apply heuristic rules for regulating the expanded data, fixing the distracting samples that contain the falsified triggers. The supplementary experiments show that the rule-based regulation is beneficial, yielding the improvement of about 1.6% F1-score for ED. We additionally prove that, instead of transfer learning from the translated ED data, the straight data combination by random pouring surprisingly performs better. %U https://aclanthology.org/2022.coling-1.232/ %P 2633-2638
Markdown (Informal)
[Unregulated Chinese-to-English Data Expansion Does NOT Work for Neural Event Detection](https://aclanthology.org/2022.coling-1.232/) (Li et al., COLING 2022)
- Unregulated Chinese-to-English Data Expansion Does NOT Work for Neural Event Detection (Li et al., COLING 2022)
ACL
- Zhongqiu Li, Yu Hong, Jie Wang, Shiming He, Jianmin Yao, and Guodong Zhou. 2022. Unregulated Chinese-to-English Data Expansion Does NOT Work for Neural Event Detection. In Proceedings of the 29th International Conference on Computational Linguistics, pages 2633–2638, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.