We introduce a method for transliteration generation that can produce transliterations in every language. Where previous results are only as multilingual as Wikipedia, we show how to use training data from Wikipedia as surrogate training for any language. Thus, the problem becomes one of ranking Wikipedia languages in order of suitability with respect to a target language. We introduce several task-specific methods for ranking languages, and show that our approach is comparable to the oracle ceiling, and even outperforms it in some cases.
We contrast our similarity metric with a measure called Weighted AVerage Entropy (WAVE) @cite_14 . WAVE measures the frequency of alphabet ngrams weighted by the entropy of the ngram mapping. However, to estimate WAVE, one needs to have a set of name pairs in @math .
@cite_14
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We use an information-theoretic notion, namely, (Shannon) information rate, to generalize common syntactic similarity metrics (like Hamming distance and longest common subsequences) between strings to ones between languages. We show that the similarity metrics between two regular languages are computable. We further study self-similarity of a regular language under various similarity metrics. As far as semantic similarity is concerned, we study the amplitude of an automaton, which intuitively characterizes how much a typical execution of the automaton fluctuates. Finally, we investigate, through experiments, how to measure similarity between two real-world programs using Lempel-Ziv compression on the runs at the assembly level.