Positional Artefacts Propagate Through Masked Language Model Embeddings

Ziyang Luo, Artur Kulmizev, Xiaoxi Mao


Abstract
In this work, we demonstrate that the contextualized word vectors derived from pretrained masked language model-based encoders share a common, perhaps undesirable pattern across layers. Namely, we find cases of persistent outlier neurons within BERT and RoBERTa’s hidden state vectors that consistently bear the smallest or largest values in said vectors. In an attempt to investigate the source of this information, we introduce a neuron-level analysis method, which reveals that the outliers are closely related to information captured by positional embeddings. We also pre-train the RoBERTa-base models from scratch and find that the outliers disappear without using positional embeddings. These outliers, we find, are the major cause of anisotropy of encoders’ raw vector spaces, and clipping them leads to increased similarity across vectors. We demonstrate this in practice by showing that clipped vectors can more accurately distinguish word senses, as well as lead to better sentence embeddings when mean pooling. In three supervised tasks, we find that clipping does not affect the performance.
Anthology ID:
2021.acl-long.413
Volume:
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
Month:
August
Year:
2021
Address:
Online
Editors:
Chengqing Zong, Fei Xia, Wenjie Li, Roberto Navigli
Venues:
ACL | IJCNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
5312–5327
Language:
URL:
https://aclanthology.org/2021.acl-long.413
DOI:
10.18653/v1/2021.acl-long.413
Bibkey:
Cite (ACL):
Ziyang Luo, Artur Kulmizev, and Xiaoxi Mao. 2021. Positional Artefacts Propagate Through Masked Language Model Embeddings. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 5312–5327, Online. Association for Computational Linguistics.
Cite (Informal):
Positional Artefacts Propagate Through Masked Language Model Embeddings (Luo et al., ACL-IJCNLP 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.acl-long.413.pdf
Video:
 https://aclanthology.org/2021.acl-long.413.mp4
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