Unbounded cache model for online language modeling with open vocabulary
Table 3: Static model trained on news 2007 and tested on data from other domains.
['Train domain', 'model', 'Test domain News', 'Test domain Commentary', 'Test domain Web', 'Test domain Wiki', 'Test domain Books']
On the other hand, the Commentary and Web datasets are shuffled. Therefore, a local cache can hardly capture the relevant statistics to significantly improve upon the static model interpolated with the unigram distribution. Indeed, the perplexity of a local cache model on these datasets when the static model is trained on the News dataset is respectively 288.5 and 593.4. In comparison, the unbounded cache model achieves on the same datasets respectively a perplexity of 191.1 and 383.4.