Abstract
Does BERT store surface knowledge in its bottom layers, syntactic knowledge in its middle layers, and semantic knowledge in its upper layers? In re-examining Jawahar et al. (2019) and Tenney et al.’s (2019a) probes into the structure of BERT, we have found that the pipeline-like separation that they asserted lacks conclusive empirical support. BERT’s structure is, however, linguistically founded, although perhaps in a way that is more nuanced than can be explained by layers alone. We introduce a novel probe, called GridLoc, through which we can also take into account token positions, training rounds, and random seeds. Using GridLoc, we are able to detect other, stronger regularities that suggest that pseudo-cognitive appeals to layer depth may not be the preferable mode of explanation for BERT’s inner workings.- Anthology ID:
- 2022.coling-1.278
- 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:
- 3143–3153
- Language:
- URL:
- https://aclanthology.org/2022.coling-1.278
- DOI:
- Bibkey:
- Cite (ACL):
- Jingcheng Niu, Wenjie Lu, and Gerald Penn. 2022. Does BERT Rediscover a Classical NLP Pipeline?. In Proceedings of the 29th International Conference on Computational Linguistics, pages 3143–3153, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
- Cite (Informal):
- Does BERT Rediscover a Classical NLP Pipeline? (Niu et al., COLING 2022)
- Copy Citation:
- PDF:
- https://aclanthology.org/2022.coling-1.278.pdf
- Code
- frankniujc/gridloc_probe
- Data
- SentEval
Export citation
@inproceedings{niu-etal-2022-bert,
title = "Does {BERT} Rediscover a Classical {NLP} Pipeline?",
author = "Niu, Jingcheng and
Lu, Wenjie and
Penn, Gerald",
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-1.278",
pages = "3143--3153",
abstract = "Does BERT store surface knowledge in its bottom layers, syntactic knowledge in its middle layers, and semantic knowledge in its upper layers? In re-examining Jawahar et al. (2019) and Tenney et al.{'}s (2019a) probes into the structure of BERT, we have found that the pipeline-like separation that they asserted lacks conclusive empirical support. BERT{'}s structure is, however, linguistically founded, although perhaps in a way that is more nuanced than can be explained by layers alone. We introduce a novel probe, called GridLoc, through which we can also take into account token positions, training rounds, and random seeds. Using GridLoc, we are able to detect other, stronger regularities that suggest that pseudo-cognitive appeals to layer depth may not be the preferable mode of explanation for BERT{'}s inner workings.",
}
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<abstract>Does BERT store surface knowledge in its bottom layers, syntactic knowledge in its middle layers, and semantic knowledge in its upper layers? In re-examining Jawahar et al. (2019) and Tenney et al.’s (2019a) probes into the structure of BERT, we have found that the pipeline-like separation that they asserted lacks conclusive empirical support. BERT’s structure is, however, linguistically founded, although perhaps in a way that is more nuanced than can be explained by layers alone. We introduce a novel probe, called GridLoc, through which we can also take into account token positions, training rounds, and random seeds. Using GridLoc, we are able to detect other, stronger regularities that suggest that pseudo-cognitive appeals to layer depth may not be the preferable mode of explanation for BERT’s inner workings.</abstract>
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%0 Conference Proceedings %T Does BERT Rediscover a Classical NLP Pipeline? %A Niu, Jingcheng %A Lu, Wenjie %A Penn, Gerald %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 niu-etal-2022-bert %X Does BERT store surface knowledge in its bottom layers, syntactic knowledge in its middle layers, and semantic knowledge in its upper layers? In re-examining Jawahar et al. (2019) and Tenney et al.’s (2019a) probes into the structure of BERT, we have found that the pipeline-like separation that they asserted lacks conclusive empirical support. BERT’s structure is, however, linguistically founded, although perhaps in a way that is more nuanced than can be explained by layers alone. We introduce a novel probe, called GridLoc, through which we can also take into account token positions, training rounds, and random seeds. Using GridLoc, we are able to detect other, stronger regularities that suggest that pseudo-cognitive appeals to layer depth may not be the preferable mode of explanation for BERT’s inner workings. %U https://aclanthology.org/2022.coling-1.278 %P 3143-3153
Markdown (Informal)
[Does BERT Rediscover a Classical NLP Pipeline?](https://aclanthology.org/2022.coling-1.278) (Niu et al., COLING 2022)
- Does BERT Rediscover a Classical NLP Pipeline? (Niu et al., COLING 2022)
ACL
- Jingcheng Niu, Wenjie Lu, and Gerald Penn. 2022. Does BERT Rediscover a Classical NLP Pipeline?. In Proceedings of the 29th International Conference on Computational Linguistics, pages 3143–3153, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.