Efficient Contextual Representation Learning With Continuous Outputs

Liunian Harold Li, Patrick H. Chen, Cho-Jui Hsieh, Kai-Wei Chang


Abstract
Contextual representation models have achieved great success in improving various downstream natural language processing tasks. However, these language-model-based encoders are difficult to train due to their large parameter size and high computational complexity. By carefully examining the training procedure, we observe that the softmax layer, which predicts a distribution of the target word, often induces significant overhead, especially when the vocabulary size is large. Therefore, we revisit the design of the output layer and consider directly predicting the pre-trained embedding of the target word for a given context. When applied to ELMo, the proposed approach achieves a 4-fold speedup and eliminates 80% trainable parameters while achieving competitive performance on downstream tasks. Further analysis shows that the approach maintains the speed advantage under various settings, even when the sentence encoder is scaled up.
Anthology ID:
Q19-1039
Volume:
Transactions of the Association for Computational Linguistics, Volume 7
Month:
Year:
2019
Address:
Cambridge, MA
Editors:
Lillian Lee, Mark Johnson, Brian Roark, Ani Nenkova
Venue:
TACL
SIG:
Publisher:
MIT Press
Note:
Pages:
611–624
Language:
URL:
https://aclanthology.org/Q19-1039
DOI:
10.1162/tacl_a_00289
Bibkey:
Cite (ACL):
Liunian Harold Li, Patrick H. Chen, Cho-Jui Hsieh, and Kai-Wei Chang. 2019. Efficient Contextual Representation Learning With Continuous Outputs. Transactions of the Association for Computational Linguistics, 7:611–624.
Cite (Informal):
Efficient Contextual Representation Learning With Continuous Outputs (Li et al., TACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/Q19-1039.pdf