Blockwise Self-Attention for Long Document Understanding

Jiezhong Qiu, Hao Ma, Omer Levy, Wen-tau Yih, Sinong Wang, Jie Tang


Abstract
We present BlockBERT, a lightweight and efficient BERT model for better modeling long-distance dependencies. Our model extends BERT by introducing sparse block structures into the attention matrix to reduce both memory consumption and training/inference time, which also enables attention heads to capture either short- or long-range contextual information. We conduct experiments on language model pre-training and several benchmark question answering datasets with various paragraph lengths. BlockBERT uses 18.7-36.1% less memory and 12.0-25.1% less time to learn the model. During testing, BlockBERT saves 27.8% inference time, while having comparable and sometimes better prediction accuracy, compared to an advanced BERT-based model, RoBERTa.
Anthology ID:
2020.findings-emnlp.232
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2020
Month:
November
Year:
2020
Address:
Online
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2555–2565
Language:
URL:
https://aclanthology.org/2020.findings-emnlp.232
DOI:
10.18653/v1/2020.findings-emnlp.232
Bibkey:
Cite (ACL):
Jiezhong Qiu, Hao Ma, Omer Levy, Wen-tau Yih, Sinong Wang, and Jie Tang. 2020. Blockwise Self-Attention for Long Document Understanding. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 2555–2565, Online. Association for Computational Linguistics.
Cite (Informal):
Blockwise Self-Attention for Long Document Understanding (Qiu et al., Findings 2020)
Copy Citation:
PDF:
https://preview.aclanthology.org/emnlp-22-attachments/2020.findings-emnlp.232.pdf
Video:
 https://slideslive.com/38940119
Code
 xptree/BlockBERT
Data
HotpotQANewsQASearchQATriviaQA