Abstract
We often use perturbations to regularize neural models. For neural encoder-decoders, previous studies applied the scheduled sampling (Bengio et al., 2015) and adversarial perturbations (Sato et al., 2019) as perturbations but these methods require considerable computational time. Thus, this study addresses the question of whether these approaches are efficient enough for training time. We compare several perturbations in sequence-to-sequence problems with respect to computational time. Experimental results show that the simple techniques such as word dropout (Gal and Ghahramani, 2016) and random replacement of input tokens achieve comparable (or better) scores to the recently proposed perturbations, even though these simple methods are faster.- Anthology ID:
- 2021.naacl-main.460
- 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
- Editors:
- Kristina Toutanova, Anna Rumshisky, Luke Zettlemoyer, Dilek Hakkani-Tur, Iz Beltagy, Steven Bethard, Ryan Cotterell, Tanmoy Chakraborty, Yichao Zhou
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 5767–5780
- Language:
- URL:
- https://aclanthology.org/2021.naacl-main.460
- DOI:
- 10.18653/v1/2021.naacl-main.460
- Cite (ACL):
- Sho Takase and Shun Kiyono. 2021. Rethinking Perturbations in Encoder-Decoders for Fast Training. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 5767–5780, Online. Association for Computational Linguistics.
- Cite (Informal):
- Rethinking Perturbations in Encoder-Decoders for Fast Training (Takase & Kiyono, NAACL 2021)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-5/2021.naacl-main.460.pdf
- Code
- takase/rethink_perturbations
- Data
- DUC 2004, WMT 2014