Abstract
This paper proposes a reinforcing method that refines the output layers of existing Recurrent Neural Network (RNN) language models. We refer to our proposed method as Input-to-Output Gate (IOG). IOG has an extremely simple structure, and thus, can be easily combined with any RNN language models. Our experiments on the Penn Treebank and WikiText-2 datasets demonstrate that IOG consistently boosts the performance of several different types of current topline RNN language models.- Anthology ID:
- I17-2008
- Volume:
- Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
- Month:
- November
- Year:
- 2017
- Address:
- Taipei, Taiwan
- Editors:
- Greg Kondrak, Taro Watanabe
- Venue:
- IJCNLP
- SIG:
- Publisher:
- Asian Federation of Natural Language Processing
- Note:
- Pages:
- 43–48
- Language:
- URL:
- https://aclanthology.org/I17-2008
- DOI:
- Cite (ACL):
- Sho Takase, Jun Suzuki, and Masaaki Nagata. 2017. Input-to-Output Gate to Improve RNN Language Models. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 43–48, Taipei, Taiwan. Asian Federation of Natural Language Processing.
- Cite (Informal):
- Input-to-Output Gate to Improve RNN Language Models (Takase et al., IJCNLP 2017)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/I17-2008.pdf
- Code
- nttcslab-nlp/iog
- Data
- Penn Treebank, WikiText-2