Morphological Analysis and Generation › Worked Example: Two Models of Turkish Vowel Harmony

Let's make the contrast concrete with a single regular phenomenon. Turkish suffixes harmonize with the last vowel of the stem: after a front vowel (e, i, ö, ü) the plural is -ler, and after a back vowel (a, ı, o, u) it is -lar. The harmony rule produces two surface suffixes for nouns covered by this regular pattern; loans and lexicalized exceptions can require additional handling.

The finite-state solution. We write the plural morpheme lexically as lAr, where A is the harmony archiphoneme, plus two two-level rules:

where:

• $A$: the archiphoneme, the underspecified vowel written lexically inside the plural suffix lAr
• $A{:}e$ / $A{:}a$: the correspondence pair, realized as the front surface vowel e or the back surface vowel a
• $\textit{FrontVowel}$ / $\textit{BackVowel}$: the symbol class of the stem's last vowel (e, i, ö, ü or a, ı, o, u)
• $C_0$: zero or more consonants between the stem's last vowel and the archiphoneme
• $\_$: the position of the $A{:}e$ or $A{:}a$ correspondence itself
• $\Leftrightarrow$: the biconditional, so the rewrite happens exactly in this environment and nowhere else

The lexicon licenses ev +PL, and the constraints select evler. The same regular harmony mapping applies productively to stems in the modeled classes, including many novel regular stems, because it references a phonological class rather than memorizing each word. Loans, proper names, consonant alternations, and lexical exceptions require additional treatment. The machinery also extends to the ablative -den/-dan, the genitive -in/-ın/-ün/-un, and buffer consonants such as the y in araba+y+a.

The neural solution. We assemble a training set: (stem, PL, surface) triples like (ev, PL, evler) and (okul, PL, okullar), plus hundreds of other stems, and train a character-level encoder-decoder. The + delimiter explicitly marks where the stem ends and the feature tag begins. What the model must infer from the examples is how much of the stem to reproduce and which surface suffix follows it. Our attention model learns this behavior without an explicit pointer-copy operation.

The code section below demonstrates both ideas, though its rule-based functions are ordinary Python rather than compiled FSTs. The interesting differences appear at the edges: an explicit rule handles a new regular stem without training examples, while the neural model may fail on an unfamiliar character pattern. Conversely, extending a hand-built grammar requires engineering the new behavior, while a neural model can learn it when enough representative examples are available. We will quantify generalization within our synthetic dataset.