Abstract
We reproduce the Structurally Constrained Recurrent Network (SCRN) model, and then regularize it using the existing widespread techniques, such as naive dropout, variational dropout, and weight tying. We show that when regularized and optimized appropriately the SCRN model can achieve performance comparable with the ubiquitous LSTM model in language modeling task on English data, while outperforming it on non-English data.- Anthology ID:
- C18-1145
- Volume:
- Proceedings of the 27th International Conference on Computational Linguistics
- Month:
- August
- Year:
- 2018
- Address:
- Santa Fe, New Mexico, USA
- Editors:
- Emily M. Bender, Leon Derczynski, Pierre Isabelle
- Venue:
- COLING
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1705–1716
- Language:
- URL:
- https://aclanthology.org/C18-1145
- DOI:
- Cite (ACL):
- Olzhas Kabdolov, Zhenisbek Assylbekov, and Rustem Takhanov. 2018. Reproducing and Regularizing the SCRN Model. In Proceedings of the 27th International Conference on Computational Linguistics, pages 1705–1716, Santa Fe, New Mexico, USA. Association for Computational Linguistics.
- Cite (Informal):
- Reproducing and Regularizing the SCRN Model (Kabdolov et al., COLING 2018)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/C18-1145.pdf
- Code
- zh3nis/scrn
- Data
- WikiText-2