Context-Aware Temperature for Language Modeling
Current practices to apply temperature scaling assume either a fixed, or a manually-crafted dynamically changing schedule. However, our studies indicate that the individual optimal trajectory for each class can change with the context. To this end, we propose context-aware temperature, a generalized approach to provide an individual optimal temperature trajectory over the context for each vocabulary, while allowing the temperature to be learned along with the remaining model parameters during training. Experiment results confirm that the proposed method significantly improves state-of-the-art language models, achieving a perplexity of 19.90 on Penn Treebank, 33.88 on WikiText-2, and 4.7 on WikiText-103.
PDF AbstractTasks
Datasets
Penn Treebank
WikiText-2
WikiText-103
Results from the Paper
Submit
results from this paper
to get state-of-the-art GitHub badges and help the
community compare results to other papers.
Methods
No methods listed for this paper. Add
relevant methods here
