  Taking advantage of computationally lightweight, but high-quality translators
prompt consideration of new applications that address neglected languages.
Locally run translators for less popular languages may assist data projects
with protected or personal data that may require specific compliance checks
before posting to a public translation API, but which could render reasonable,
cost-effective solutions if done with an army of local, small-scale pair
translators. Like handling a specialist's dialect, this research illustrates
translating two historically interesting, but obfuscated languages: 1)
hacker-speak ("l33t") and 2) reverse (or "mirror") writing as practiced by
Leonardo da Vinci. The work generalizes a deep learning architecture to
translatable variants of hacker-speak with lite, medium, and hard vocabularies.
The original contribution highlights a fluent translator of hacker-speak in
under 50 megabytes and demonstrates a generator for augmenting future datasets
with greater than a million bilingual sentence pairs. The long short-term
memory, recurrent neural network (LSTM-RNN) extends previous work demonstrating
an English-to-foreign translation service built from as little as 10,000
bilingual sentence pairs. This work further solves the equivalent translation
problem in twenty-six additional (non-obfuscated) languages and rank orders
those models and their proficiency quantitatively with Italian as the most
successful and Mandarin Chinese as the most challenging. For neglected
languages, the method prototypes novel services for smaller niche translations
such as Kabyle (Algerian dialect) which covers between 5-7 million speakers but
one which for most enterprise translators, has not yet reached development. One
anticipates the extension of this approach to other important dialects, such as
translating technical (medical or legal) jargon and processing health records.
