What language pairs are under-served by current resources — like human translators, bilingual dictionaries, and parallel corpuses — relative to their linguistic importance, economic potential, or human necessity?
There is research on using machine learning techniques to construct bilingual dictionaries from monolingual corpuses using representations like word embedding models. These techniques seem flexible enough that one could use them to relate arbitrary language pairs. The research would maybe have a bigger impact if it targeted under-served language pairs first.
Now, linguistic importance drives linguists to build resources. Economic potential motivates private industry. Cultural similarity or geographical proximity produces more humans who know related pairs of languages. But I suspect these effects aren't uniform. I'm interested in insight from linguists as to languages for which the tools don't measure up to the need.