Tensorized Embedding Layers

Oleksii Hrinchuk, Valentin Khrulkov, Leyla Mirvakhabova, Elena Orlova, Ivan Oseledets


Abstract
The embedding layers transforming input words into real vectors are the key components of deep neural networks used in natural language processing. However, when the vocabulary is large, the corresponding weight matrices can be enormous, which precludes their deployment in a limited resource setting. We introduce a novel way of parameterizing embedding layers based on the Tensor Train decomposition, which allows compressing the model significantly at the cost of a negligible drop or even a slight gain in performance. We evaluate our method on a wide range of benchmarks in natural language processing and analyze the trade-off between performance and compression ratios for a wide range of architectures, from MLPs to LSTMs and Transformers.
Anthology ID:
2020.findings-emnlp.436
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2020
Month:
November
Year:
2020
Address:
Online
Venues:
EMNLP | Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4847–4860
Language:
URL:
https://aclanthology.org/2020.findings-emnlp.436
DOI:
10.18653/v1/2020.findings-emnlp.436
Bibkey:
Cite (ACL):
Oleksii Hrinchuk, Valentin Khrulkov, Leyla Mirvakhabova, Elena Orlova, and Ivan Oseledets. 2020. Tensorized Embedding Layers. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 4847–4860, Online. Association for Computational Linguistics.
Cite (Informal):
Tensorized Embedding Layers (Hrinchuk et al., Findings 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.findings-emnlp.436.pdf
Data
IMDb Movie ReviewsSSTWikiText-103WikiText-2