Abstract
This paper proposes a state-of-the-art recurrent neural network (RNN) language model that combines probability distributions computed not only from a final RNN layer but also middle layers. This method raises the expressive power of a language model based on the matrix factorization interpretation of language modeling introduced by Yang et al. (2018). Our proposed method improves the current state-of-the-art language model and achieves the best score on the Penn Treebank and WikiText-2, which are the standard benchmark datasets. Moreover, we indicate our proposed method contributes to application tasks: machine translation and headline generation.- Anthology ID:
- D18-1489
- Volume:
- Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
- Month:
- October-November
- Year:
- 2018
- Address:
- Brussels, Belgium
- Editors:
- Ellen Riloff, David Chiang, Julia Hockenmaier, Jun’ichi Tsujii
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 4599–4609
- Language:
- URL:
- https://aclanthology.org/D18-1489
- DOI:
- 10.18653/v1/D18-1489
- Cite (ACL):
- Sho Takase, Jun Suzuki, and Masaaki Nagata. 2018. Direct Output Connection for a High-Rank Language Model. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 4599–4609, Brussels, Belgium. Association for Computational Linguistics.
- Cite (Informal):
- Direct Output Connection for a High-Rank Language Model (Takase et al., EMNLP 2018)
- PDF:
- https://preview.aclanthology.org/naacl24-info/D18-1489.pdf
- Code
- nttcslab-nlp/doc_lm
- Data
- Penn Treebank, WikiText-2