Abstract
We present a new architecture for named entity recognition. Our model employs multiple independent bidirectional LSTM units across the same input and promotes diversity among them by employing an inter-model regularization term. By distributing computation across multiple smaller LSTMs we find a significant reduction in the total number of parameters. We find our architecture achieves state-of-the-art performance on the CoNLL 2003 NER dataset.- Anthology ID:
- P18-2012
- Volume:
- Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
- Month:
- July
- Year:
- 2018
- Address:
- Melbourne, Australia
- Editors:
- Iryna Gurevych, Yusuke Miyao
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 69–74
- Language:
- URL:
- https://aclanthology.org/P18-2012
- DOI:
- 10.18653/v1/P18-2012
- Cite (ACL):
- Andrej Žukov-Gregorič, Yoram Bachrach, and Sam Coope. 2018. Named Entity Recognition With Parallel Recurrent Neural Networks. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 69–74, Melbourne, Australia. Association for Computational Linguistics.
- Cite (Informal):
- Named Entity Recognition With Parallel Recurrent Neural Networks (Žukov-Gregorič et al., ACL 2018)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/P18-2012.pdf