Abstract
We present a language model that combines a large parametric neural network (i.e., a transformer) with a non-parametric episodic memory component in an integrated architecture. Our model uses extended short-term context by caching local hidden states—similar to transformer-XL—and global long-term memory by retrieving a set of nearest neighbor tokens at each timestep. We design a gating function to adaptively combine multiple information sources to make a prediction. This mechanism allows the model to use either local context, short-term memory, or long-term memory (or any combination of them) on an ad hoc basis depending on the context. Experiments on word-based and character-based language modeling datasets demonstrate the efficacy of our proposed method compared to strong baselines.- Anthology ID:
- 2021.tacl-1.22
- Volume:
- Transactions of the Association for Computational Linguistics, Volume 9
- Month:
- Year:
- 2021
- Address:
- Cambridge, MA
- Editors:
- Brian Roark, Ani Nenkova
- Venue:
- TACL
- SIG:
- Publisher:
- MIT Press
- Note:
- Pages:
- 362–373
- Language:
- URL:
- https://aclanthology.org/2021.tacl-1.22
- DOI:
- 10.1162/tacl_a_00371
- Cite (ACL):
- Dani Yogatama, Cyprien de Masson d’Autume, and Lingpeng Kong. 2021. Adaptive Semiparametric Language Models. Transactions of the Association for Computational Linguistics, 9:362–373.
- Cite (Informal):
- Adaptive Semiparametric Language Models (Yogatama et al., TACL 2021)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/2021.tacl-1.22.pdf