  Fixed-vocabulary language models fail to account for one of the most
characteristic statistical facts of natural language: the frequent creation and
reuse of new word types. Although character-level language models offer a
partial solution in that they can create word types not attested in the
training corpus, they do not capture the "bursty" distribution of such words.
In this paper, we augment a hierarchical LSTM language model that generates
sequences of word tokens character by character with a caching mechanism that
learns to reuse previously generated words. To validate our model we construct
a new open-vocabulary language modeling corpus (the Multilingual Wikipedia
Corpus, MWC) from comparable Wikipedia articles in 7 typologically diverse
languages and demonstrate the effectiveness of our model across this range of
languages.
