The Rosetta Stone was one of the most important documents in the history of linguistics. Discovered around 1800, it allowed Ancient Egyptian to be deciphered. Let's say that the stone didn't exist, was destroyed like the Library of Alexandria, or hadn't been found yet. Knowing that Ancient Egyptian is an Afroasiatic language, could the hieroglyphs have been deciphered using the other Afroasiatic languages which were known (Chadic, Cushitic, Semitic, etc.) and some hard cryptanalysis?
Fundamentally, Hieroglyphic Egyptian was cracked by Champollion, using two hypotheses.
- Egyptian is the ancestor of Coptic.
- The names in the "cartouches" are royal names. These hypotheses gradually led to a consistent set of readings for the hieroglyphs, that ultimately enabled to read all inscriptions.
The fact that Egyptian belongs to Afrasian is a consequence of the deciphering, and played no role in the deciphering itself. In all cases, the branches of Afrasian are too different from each other to be of any help.
If you take Old Nubian, it's still very hard to read and understand, even though it's related to present-day Nubian. So difficulties remain, even when the language to decipher is a direct ancestor.
The advantage of the Rosetta Stone is that the same text is written in three different languages. Without such a document, deciphering would certainly be possible, but considerably more difficult and, what is more, hard to prove it's a real deciphering.
Possibly (but probably not)!
Translating between two languages with only monolingual corpora (unsupervised machine translation) is currently possible. It's an area of active research in NLP because current machine translation methods use large, parallel sentences which are expensive to create and don't exist between many language pairs.
The current state-of-the-art in unsupervised translation is Song et al. (2019), which reports BLEU of 37.5 on English-French. For reference, Google Translate, which uses parallel data, only scored about 35.7 as of 2017 (higher BLEU is better) (Johnson et al., 2017).
However, EN-FR is one of the easiest pairs because:
- There is a lot of high-quality parallel and non-parallel data since both are official languages of the U.N., the E.U., various countries, etc., are spoken by millions of people worldwide
- The languages have many cognates and some shared vocabulary
- The languages share a fairly simple writing system (esp. compared to hieroglyphics)
Lample et al. (2018) tested their system on Urdu->English, two unrelated languages with different writing systems and with (relatively) little available data, and obtain 12.3 BLEU. I don't have a reference point for how good that is, but it's definitely a start.
Finally, Zhang et al. (2019) train a translation system on Chinese -> Japanese, and show that it is possible to learn information about logographic writing systems, but Japanese kanji is borrowed from Chinese characters, so there's a lot of shared vocabulary.
That being said, hieroglyphs are (IMO) a more complicated writing system than even Chinese. And even for English-Urdu, a "low-resource" language pair, Lample et al. use 5.5M sentences. I have no idea how much text exists in hieroglyphs, but suspect it's less than this. But in theory, if we dug up and digitized millions of tablets of an ancient, unknown language, then yes, we do have tools to translate it.