Question: Why is the data-driven sequence-to-sequence model chosen?
Evidence: g2p is the problem of converting the orthographic representation of a word into a phonemic representation. A phoneme is an abstract unit of sound which may have different realizations in different contexts. For example, the English phoneme has two phonetic realizations (or allophones):
English speakers without linguistic training often struggle to perceive any difference between these sounds. Writing systems usually do not distinguish between allophones: and are both written as INLINEFORM0 p INLINEFORM1 in English. The sounds are written differently in languages where they contrast, such as Hindi and Eastern Armenian.
Most writing systems in use today are glottographic, meaning that their symbols encode solely phonological information. But despite being glottographic, in few writing systems do graphemes correspond one-to-one with phonemes. There are cases in which multiple graphemes represent a single phoneme, as in the word the in English:

There are cases in which a single grapheme represents multiple phonemes, such as syllabaries, in which each symbol represents a syllable.
In many languages, there are silent letters, as in the word hora in Spanish:

There are more complicated correspondences, such as the silent e in English that affects the pronunciation of the previous vowel, as seen in the pair of words cape and cap.
It is possible for an orthographic system to have any or all of the above phenomena while remaining unambiguous. However, some orthographic systems contain ambiguities. English is well-known for its spelling ambiguities. Abjads, used for Arabic and Hebrew, do not give full representation to vowels.
Consequently, g2p is harder than simply replacing each grapheme symbol with a corresponding phoneme symbol. It is the problem of replacing a grapheme sequence INLINEFORM0 
with a phoneme sequence INLINEFORM0 
where the sequences are not necessarily of the same length. Data-driven g2p is therefore the problem of finding the phoneme sequence that maximizes the likelihood of the grapheme sequence: INLINEFORM0 
Data-driven approaches are especially useful for problems in which the rules that govern them are complex and difficult to engineer by hand. g2p for languages with ambiguous orthographies is such a problem. Multilingual g2p, in which the various languages have similar but different and possibly contradictory spelling rules, can be seen as an extreme case of that. Therefore, a data-driven sequence-to-sequence model is a natural choice.
Data-driven approaches are especially useful for problems in which the rules that govern them are complex and difficult to engineer by hand. g2p for languages with ambiguous orthographies is such a problem. Multilingual g2p, in which the various languages have similar but different and possibly contradictory spelling rules, can be seen as an extreme case of that. Therefore, a data- driven sequence-to-sequence model is a natural choice.
Answer: Data-driven approaches are especially useful for problems in which the rules that govern them are complex and difficult to engineer by hand. g2p for languages with ambiguous orthographies is such a problem. Multilingual g2p, in which the various languages have similar but different and possibly contradictory spelling rules, can be seen as an extreme case of that. Therefore, a data- driven sequence-to-sequence model is a natural choice.