Abstract
Word ordering is a constrained language generation task taking unordered words as input. Existing work uses linear models and neural networks for the task, yet pre-trained language models have not been studied in word ordering, let alone why they help. We use BART as an instance and show its effectiveness in the task. To explain why BART helps word ordering, we extend analysis with probing and empirically identify that syntactic dependency knowledge in BART is a reliable explanation. We also report performance gains with BART in the related partial tree linearization task, which readily extends our analysis.- Anthology ID:
- 2022.coling-1.567
- 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:
- 6516–6529
- Language:
- URL:
- https://aclanthology.org/2022.coling-1.567
- DOI:
- Bibkey:
- Cite (ACL):
- Zebin Ou, Meishan Zhang, and Yue Zhang. 2022. On the Role of Pre-trained Language Models in Word Ordering: A Case Study with BART. In Proceedings of the 29th International Conference on Computational Linguistics, pages 6516–6529, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
- Cite (Informal):
- On the Role of Pre-trained Language Models in Word Ordering: A Case Study with BART (Ou et al., COLING 2022)
- Copy Citation:
- PDF:
- https://aclanthology.org/2022.coling-1.567.pdf
- Code
- simtony/bart-word-orderer
- Data
- CommonGen
Export citation
@inproceedings{ou-etal-2022-role,
title = "On the Role of Pre-trained Language Models in Word Ordering: A Case Study with {BART}",
author = "Ou, Zebin and
Zhang, Meishan and
Zhang, Yue",
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-1.567",
pages = "6516--6529",
abstract = "Word ordering is a constrained language generation task taking unordered words as input. Existing work uses linear models and neural networks for the task, yet pre-trained language models have not been studied in word ordering, let alone why they help. We use BART as an instance and show its effectiveness in the task. To explain why BART helps word ordering, we extend analysis with probing and empirically identify that syntactic dependency knowledge in BART is a reliable explanation. We also report performance gains with BART in the related partial tree linearization task, which readily extends our analysis.",
}
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%0 Conference Proceedings %T On the Role of Pre-trained Language Models in Word Ordering: A Case Study with BART %A Ou, Zebin %A Zhang, Meishan %A Zhang, Yue %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 ou-etal-2022-role %X Word ordering is a constrained language generation task taking unordered words as input. Existing work uses linear models and neural networks for the task, yet pre-trained language models have not been studied in word ordering, let alone why they help. We use BART as an instance and show its effectiveness in the task. To explain why BART helps word ordering, we extend analysis with probing and empirically identify that syntactic dependency knowledge in BART is a reliable explanation. We also report performance gains with BART in the related partial tree linearization task, which readily extends our analysis. %U https://aclanthology.org/2022.coling-1.567 %P 6516-6529
Markdown (Informal)
[On the Role of Pre-trained Language Models in Word Ordering: A Case Study with BART](https://aclanthology.org/2022.coling-1.567) (Ou et al., COLING 2022)
- On the Role of Pre-trained Language Models in Word Ordering: A Case Study with BART (Ou et al., COLING 2022)
ACL
- Zebin Ou, Meishan Zhang, and Yue Zhang. 2022. On the Role of Pre-trained Language Models in Word Ordering: A Case Study with BART. In Proceedings of the 29th International Conference on Computational Linguistics, pages 6516–6529, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.