Multi-Grained Knowledge Distillation for Named Entity Recognition

Xuan Zhou, Xiao Zhang, Chenyang Tao, Junya Chen, Bing Xu, Wei Wang, Jing Xiao


Abstract
Although pre-trained big models (e.g., BERT, ERNIE, XLNet, GPT3 etc.) have delivered top performance in Seq2seq modeling, their deployments in real-world applications are often hindered by the excessive computations and memory demand involved. For many applications, including named entity recognition (NER), matching the state-of-the-art result under budget has attracted considerable attention. Drawing power from the recent advance in knowledge distillation (KD), this work presents a novel distillation scheme to efficiently transfer the knowledge learned from big models to their more affordable counterpart. Our solution highlights the construction of surrogate labels through the k-best Viterbi algorithm to distill knowledge from the teacher model. To maximally assimilate knowledge into the student model, we propose a multi-grained distillation scheme, which integrates cross entropy involved in conditional random field (CRF) and fuzzy learning.To validate the effectiveness of our proposal, we conducted a comprehensive evaluation on five NER benchmarks, reporting cross-the-board performance gains relative to competing prior-arts. We further discuss ablation results to dissect our gains.
Anthology ID:
2021.naacl-main.454
Volume:
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
June
Year:
2021
Address:
Online
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
5704–5716
Language:
URL:
https://aclanthology.org/2021.naacl-main.454
DOI:
10.18653/v1/2021.naacl-main.454
Bibkey:
Cite (ACL):
Xuan Zhou, Xiao Zhang, Chenyang Tao, Junya Chen, Bing Xu, Wei Wang, and Jing Xiao. 2021. Multi-Grained Knowledge Distillation for Named Entity Recognition. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 5704–5716, Online. Association for Computational Linguistics.
Cite (Informal):
Multi-Grained Knowledge Distillation for Named Entity Recognition (Zhou et al., NAACL 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.naacl-main.454.pdf
Video:
 https://aclanthology.org/2021.naacl-main.454.mp4
Code
 11zhouxuan/multi_grained_kd_ner