EMNLP 2021main45 citations
SHAPE: Shifted Absolute Position Embedding for Transformers
Shun Kiyono, Sosuke Kobayashi, Jun Suzuki, Kentaro Inui
Abstract
Position representation is crucial for building position-aware representations in Transformers. Existing position representations suffer from a lack of generalization to test data with unseen lengths or high computational cost. We investigate shifted absolute position embedding (SHAPE) to address both issues. The basic idea of SHAPE is to achieve shift invariance, which is a key property of recent successful position representations, by randomly shifting absolute positions during training. We demonstrate that SHAPE is empirically comparable to its counterpart while being simpler and faster.
BibTeX
@inproceedings{kiyono-etal-2021-shape,
title = "{SHAPE}: {S}hifted Absolute Position Embedding for Transformers",
author = "Kiyono, Shun and
Kobayashi, Sosuke and
Suzuki, Jun and
Inui, Kentaro",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.266/",
doi = "10.18653/v1/2021.emnlp-main.266",
pages = "3309--3321"
}